### si 

# I11ll1111II11111111111111111Il11IIIIII 11111111

# 111ll1l Il1

# 1111111111

# 1111 NUREGCR2220Vl

E I 

# IMPACT 

# OF ENTRAINMENTAND IMPINGEMENT FISH POPULATIONS INTHE HUDSON RIVER 

# NARY.VOLUME 

# I: ENTRAINMENTIMPACT ESTIMATES 

# FOR SIX FISH POPULATIONS INHABITINGTHE HUDSON RIVER ESTUARY

OAK RIDGE 

### NATIONAL LAB., TN

JAN 1982

US.

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### NURRG 

### CR2220V1 

# 111llllllllllClllllllllliillllllllll~llllll111 Volume 

### I ORNL/NUREG/TM-385/Vl Distribution Category-RE
Contract No. 

### W-7405-eng-26



### THE IMPACT OF ENTRAINMENT

### AND IMPINGEMENT

### ON FISH POPULATIONS 

### IN THE HUDSON RIVER ESTUARY VOLUME 

### I Entrainment Impact Estimates For Six Fish Populations Inhabiting the Hudson River Estuary



### J. Boreman,l 

### L. W. 

### Barnthouse, D. 

### S. Vaughan, C. 

### P. Goodyear,:! 

### S. W. 

### Christensen, 

### K. D. Kumar, B. 

### L. Kirk, and 

### W. Van Winkle



### ENVIRONMENTAL SCIENCES DIVISION Publication No. 

### 1790

INortheast Fisheries Center

## ,National Marine Fisheries Service,

Woods 

### Hole, Massachusetts 

### 02543. 2National Fish Center--Leetown, U.S. Fish and Wildlife Service,

Route 

### 3, P.O. Box 

### 41, Kearneysville, West Virginia 25430.


### Manuscript Completed 

# - September 1981 Date Published

# - January 1982
Prepared for Office of 

### Nuclear Regulatory Research

U.S. 

### Nuclear Regulatory Commission Washington, 

### D.C 20555 Under Interagency Agreement No. DOE 40-550-75


### NRC FIN BO165
TASK: Methods to Assess Impacts on 

### Hudson River Striped Bass
OAK 

### RIDGE NATIONAL LABORATORY

Oak Ridge, Tennessee 37830 

### operated by 

### UNION CARBIDE CORPORATION for the DEPARTMENT OF ENERGY

REPRODUCEDBY US.DEPARTMENTOFCOMMERCE NATIOt4P.LTECHNICAL INFORMATIONSERVICE SPRINGFIELD,VA22161

### BIBLIOGRAPHIC INFORMATION

NUREG/CR-2220-V I 

### The Impact 

### of Entrainment and 

### Implingement 

### on Fish Populations in the 

### Hudson 

### River Estuary. 

### Volume I: Entrainment Impact 

### Estimates 

### for Six 

### Fish Populations Inhabiting the Hudson 

### River Estuary, Jan 82 J. Boreman, L. 

### U. Barnthouse, D. S. 

### Vaughan, C. 

### P. 

### Goodyear, and S. 

### W. 

### Christensen. PERFORMER: Oak Ridge National Lab., 

### TN. ORNL/N

### UREG/

### TM

### -385/B 1 

### Contract W-7405-eng-26 SPONSOR: Nuclear Regulatory 

### Commission, Washington, DC. 

### Off 

### ice

### -of Hucleer Regulatory 

### Research

# . Pub. 

### as Environmental Sciences Div. Pub-1790. Prepared in cooperation with National 

### Marine Fisheries 

### Service, 

### Woods Hole, MA. 

### Northeast Fisheries 

### Center, and Fish and Nildlife Service, Kearneysville, WV. 

### The report 

### is concerned with the 

### estimation of the direct 

### (or annual) entrainment impact 

### of the 

### power plants on ' 

### populations 

### of striped bass, white perch, 

### Alosa spp. (blueback herring and 

### alewife), American shad, Atlantic tomcod, and bay 

### anchovy 

### in the Hudson River. Entrainment impact results from the 

### killing of 

### fish eggs, larvae, and young juveniles that 

### are contained in the coolingwater cycled through 

### a power plant. 

### An 'Empirical Transport Model' 

### is presented 

### as the 

### means of obtaining 

### a conditional entrainment 

### mortality rate. KEYWORDS: *Entrainment, "Fishes, "Intake 

### systems, *Hudson 

### River Estuary, "Thermal pollution. Available 

### from the National Technical Information Service, Springfield, 

### Va. 22161 PRICE CODE: PC 

### A18/MF A01

i 

# 7' ACKNOWLEDGMENTS



### We 

### thankDrs. 

### G. 

### F. Cada and C. 

### C. Coutant 

### (ORNL), Paul Rag0 

### (National Power 

### Plant Team), Marcia Mulkey 

### (NRC), and Barry 

### Cohen, Tom 

### Fikslin, Henry Gluckstern, and Jonathan Strong (EPA Region

### V) for their 

### careful 

### review of the 

### testimony published in

### this report.

.- . _-__. . . . . ii

### i



# Precedingpage

# blankFOREWORD



### On December 19, 1980, with the signing of 

### an out-of-court settlement agreement, a three-year adjudicatory hearing on the effects 

### of electric power generation on the Hudson River was ended. The purpose 

### of this hearing had been to determine whether six cooling towers, required 

### by the various Environmental Protection Agency 

### (�PA) permits, should be built at three power plants on the Hudson River in New York 

### in order tomitigate the impacts of entrainment and impingement on estuarine fish populations. In addition to terminating the 

### EPA hearings, the settlement resolved regulatory disputes between the utility companies and several other federal agencies, including the U.S. Nuclear Regulatory Commission (NRC). 

### Staff of 

### the Environmental Sciences Division at Oak Ridge National Laboratory (ORNL) 

### were asked to participate in the EPA 

### hearings because of previous work on entrainment and impingement performed for NRC 

### in connection with the licensing of Indian Point Units 2 and 

### 3, the largest generating units on the Hudson 

### River. 

### ORNL Staff prepared and submitted, in May 1979, numerous individual pieces of written direct testimony for EPA 

### as part of 

### these hearings. Some of 

### these pieces 

### of testimony were coauthored with individuals from the National Power Plant Team of the 

### U. S. 

### Fish and Wildlife Service and from EPA. The purpose of this three-volume report is to publish these individual pieces of testimony involving ORNL 

### staff in a manner that will assure a broader distribution to the 

### scientific community, government agencies, and other interested parties.



## - Volume 

### I is concerned with the estimation of 

### the direct (or

annual) entrainment impact of 

### the power plants on 

### populations of 

### striped bass, white perch, Alosa spp. (blueback herring and alewife), American shad, Atlantic tomcod, and bay anchovy in the Hudson River. Entrainment impact results from 

### the killing of 

### fish eggs, larvae, and young juveniles that are contained in the 

### cooling water cycled through

a 

### power plant. An "Empirical Transport Model" is presented as the
means of 

### obtaining a conditional entrainment mortality rate (which represents the fraction of a year class which would be killed due to

V 

# Preceding

# page-blank

­

. -

### vi

entrainment in the absence of density-dependent mortality). Most 

### of ' 

### Volume 

### I is concerned with the estimation of several parameters required 

### by themodel: physical input parameters (e.g., power-plant withdrawal flow rates); the longitudinal distribution 

### of ichthyoplankton in time and space; the duration of susceptibility 

### of the vulnerable organisms ;

### the "W-factors 

## ,"which express the ratios of 

### densities of organisms in power plant intakes to densities in the river; and the entrainment mortality factors, which express the probability that an organism 

### will be killed 

### if it 

### is 

### entrained. Once these values are obtained, themodel is used to estimate entrainment impact 

### forboth historical conditions and projected conditions. Volume 

### I1 contains four exhibits relating to 

### impingement impacts and three critiques of 

### certain aspects of the utilities' case. The first exhibit is a quantitative evaluation of 

### four sources of 

### bias (collection efficiency, reimpingement, impingement on inoperative screens, and impingement survival) affecting estimates of 

### the number of 

### fish killed at Hudson River power plants. The two 

### following exhibits contain, respectively, a detailed assessment of the impact of 

### impingement on the Hudson River white perch population and estimates of 

### conditional impingement mortality rates for 

### seven Hudson River fish populations. 

### The fourth exhibit is an evaluation 

### of the engineering feasibility and potential biological effectiveness of 

### several types of modified intake structures proposed as alternatives to cooling towers for reducing impingement impacts. The reuiainder of 

### Volume 11 

### consists of 

### critical evaluations of the utilities' empirical evidence for the existence of density-dependent growth in 

### young-of-the-year striped bass and white perch, 

### of their estimate 

### of the age-composition of the striped bass spawning stock 

### in the Hudson River, and of their use 

### of the Lawler, Matusky, and Skelly (LMS) Real-Time Life Cycle Model to estimate the impact of 

### entrainment and impingement on the Hudson River striped bass 

### population. Volume 111 

### addresses the validity of the utilities' use of 

### the

. - 

### Ricker stock-recruitment model to 

### extrapolate the 

### combined entrainment-impingement losses of 

### young fish to 

### reductions in the equilibrium population size of 

### adult fish. In our testimony, a

I ,

### vii

methodology was developed and applied 

### to address a single fundamental question: 

### if the Ricker model really 

### did apply to 

### the Hudson River striped bass population, could the utilities' estimates, based on curve-fitting, of 

### the parameter alpha (which controls the impact) be considered reliable? The present Volume 111 includes, in addition, an analysis of the efficacy 

### of an alternative means of 

### estimating alpha, termed the technique 

### of prior estimation 

### ofbeta (used by the utilities 

### in a report prepared for regulatory hearings on the Cornwall Pumped Storage Project). Our validation methodology should also be useful in evaluating inferences drawn 

### in the literature 

### from fits of stock-recruitment models to data obtained from other fish stocks.SUMMARY



### The first step in assessing how losses due to entrainrnent mortality may affect the futurewell-being of a fish population 

### is calculating the conditional entrainment mortality imposed by power plants on that population. The conditional entrainment mortality rate is defined as the fraction of 

### a population which would be killed due to entrainment in the absence of any other source of mortality. 

### In this exhibit, historical and projected conditional entrainment mortality rates are estimated for six fish populations inhabiting the Hudson River estuary: striped bass, white perch, Alosa spp. (blueback herring 

# - and alewife), American shad, Atlantic tomcod, and bay anchovy. Chapter 

### I, entitled "Mathematical Methods Used in Estimating Conditional Entrainment Mortality Rates of Six Hudson River Fish Populations," presents the Empirical Transport Model 

### (ETM)

## ,which is themathematical method used in this exhibit. Appendix A, 

### which is available under separate cover, describes the derivation of 

### the ETM 

### in greater detail and provides a hypothetical example 

### of how the

### model operates.. Chapter I1 

### of this exhibit is entitled "Physical Input Parameter Values Used to Estimate Conditional Entrainment Mortality Rates for 

### Six Rudson River Fish Populations." This chapter presents the physical input parameter values used in the 

### ETM. The physical parameters are the river region volumes, historical and projected power plant withdrawal flow rates, and the proportion of 

### power plant water withdrawn frcm each river region. These values are used for ETM 

### analyses of all six fish populations. Chapter I11 is 

### entitled "Spatial Distributions of Entrainable Life Stages of Six Hudson River Fish Populations." These distributions are used as input to the ETM and are based on 

### 1974 and 

### 1975 Texas Instruments Inc. 

### (TI) field data. Chapter IV, entitled "Durations of 

### the Entrainment Intervals and Entrainment Periods for Six Hudson River Fish 

### Populations," presents values used in the ETM for the total period of time individualmembers

xi 

# Precedingpage

# blank

### xii

of 

### a given population take togrow through entrainable life stages (entraiment interval) and the range of calendar dates that entrainable individuals 

### are present 

### in the Hudson River (entrainment period). Chapter

### V is entitled "W-Factors forHudson River lchthyoplankton Entrained at Bowline, Lovett, IndianPoint, Roseton, and Danskammer." This chapter presents two alternatsvemethods of calculating the W-factors (ratios of power plant intakeorganism densities 

### to river densities) used inthe

### ETM- Appendix 

### B, which accompanies this chapter, discusses the sources of dataused tocompute U-factors using the two methods. Chapter VI, entitled "Estimating the Ratio of Power Plant Intake Organism Densities toRiver Densities Using the River Data Methodology," presents a third approach toestimating W-factors. approach was used when lack 

### of sufficient data prohibited use of either This

of the two approaches inChapter 

### V. Chapter VI1

### is entitled "Entrainment MortalityFactors forHudson River Lchthyoplankton 

### at Bowline Point, Lovett, Indian Point, Roseton, and Danskammer Point Power Plants." Appendices C-F accompany this chapter and provide the sources of data and mathematical justifications 

### for the entrainment mortality factors (f-factors) as they are used in the ETM. Appendix 

### P, which alsorelates tothis topic, summarizes indirect 

### or sublethaleffects and describes a method of incorporating such effectsinto entrainment mortalityfactor calculations. Chapter VI11presents the results 

### of the ETM runs using the input parameter values described inChapters 

### 11-VII. Appendices G-0, which accompany this chapter, 

### list detailed

### ETM run results for each 

### of the 

### six fish populations. r.7

### ABSTRACT
BOREMAN, 

### J., 

### L. W. 

### BARNTHOUSE, D. S. VAUGHAN, C. 

### P. GOODYEAR, 

### S. W. CHRISTENSEN, 

### R. D. KUMAR, 

### B. L. KIRK, and W. 

### VAN

### WINKLE. 1981. Entrainment impact estimates for six fish populations inhabiting the Hudson River Estuary. Volume 

### I. The Impact of 

### Entrainment and Impingement 

### on Fish Populations in the Hudson River Estuary. ORNL/NUREG/TM-385/Vl and NUREG/CR-2220. Oak Ridge National Laboratory, Oak Ridge, Tennessee.

This volume is concerned with the estimation of 

### the direct (or annual) entrainment impact of 

### power plants on populations of striped bass

## ,white perch, 

# - Alosa spp. (blueback herring and alewife), American shad, Atlantic tomcod, and bay anchavy in the Hudson River estuary. Entrainment impact results from the killing of 

### fish eggs, larvae, and young juveniles that are contained in the cooling water cycled through


### a power plant. An 

### Empirical Transport Model 

### (ETM) is presented as the
means of 

### estimating a conditional entrainment mortality rate (defined


### as the fraction of a year class which would be killed due to entrainment in the absence of 

### any other source of 

### mortality). Most 

### of this volume is concerned with the estimation of 

### several parameters required by the ETM: physical input parameters (e.g., 

### power-plant withdrawal flow rates); the longitudinal distribution of ichthyoplankton in time and space; the duration of 

### susceptibility of 

### &hevulnerable organisms; the

### W-factors, which express the ratios of densities of 

### organisms in power plant intakes to 

### densities of organisms in the river; and the entrainment mortality factors (f-factors), which express the probability that an 

### organism will be killed if it 

### is entrained. Once these values are obtained, the 

### ETM is used to estimate entrainment impact 

### for both historical and projected conditions.

ix 

# Precedingpage

# blank

-.. 

### -

### TABLE 

### OF CONTENTS Chapter Page


### I Mathematical Methods Used in Estimating Conditional Entrainment Mortality Rates of 

### Six Rudson River Fish Populations 

### (J. Boreman, C. 

### P. Goodyear, and 

### S. W. Christensen) 

# ................... x-1
11 

### Physical Input Parameter Values Used to Estimate Conditional Entrainment Mortality Rates 

### for Six Hudson River Fish Populations (J. 

### Boreman) 

# . .. 11-1
111 

### Spatial Distributions 

### of Entrainable Life Stages 

### of Six Hudson River Fish Populations 

### (J. Boreman) 

# . 111-1
IV Durations of 

### the Entrainment Intervals and Entrainment Periods for Six Hudson River Fish Populations 

### (J. Boreman) 

# ................ IV-1


### V W-factors for 

### Hudson River Ichthyoplankton Entrained.at Bowline, Lovett, Indian Point, Roseton, and Danskammer 

### (L. W. 

### Barnthouse, 

### K. D. Kumar, and B. 

### L. Kirk)

# .............. v-1
VI Estimating Ratios 

### of Power 

### Plant Intake Organism Densities to 

### River Densities Using the River'Data Methodology 

### (J. Boreman) 

# ................ VI-1
VII Entrsinment Mortality Factors for Hudson River Ichthyoplankton at Bowline Point, Lovett, Indian Point, Roseton, and Danskammer Point Power Plants 

### (D. S. Vaughan) 

# .................... VII-1


### VI11 

### Conditional Entrainment Mortality Rate Estimates for Six Hudson River Fish Populations 

### (J. Boreman and 

### C. P. Goodyear 

# ..................VIII-1
APPENDICES


### A An Empirical-TransportModel*ForEvaluating Entrainment of Aquatic Organisms by Power Plants. FWS/OBS-78/90. (3. 

### Boreman, C. 

### P. Goodyear, and 

### S. W. 

### Christensen) Under Separate Cover


### B Sources of 

### Data Used to Computer W-factors-Using the 

### MU and GBC 

### Methods 

### (L. W. Barnthouse, 

### K. D. Kumar, and 8. 

### L. Kirk) 

# ...................... B-1
C 1. 

### Derivations of 

### Component f-factors 2. 

### Development of 

### the Compromise f-factor (D. S. 

### Vaughan) 

# .................. c-1

xiiixiv



### Chapter


### D

E



### F

G-L

M-0



### P Detectability and Precision of f-?actor Estimates 

### (D. S. Vaughan) 

# .................... Additional Biases in Estimates of the f-factor 

### (DoS.Vaughan) 

# .................... Thermal 

### Model f-factor for Four Hudson River Power Plants 

### (D. S.. Vaughan)

# .............. ETH Run Results Incorporating Pooled f-factors 

### (J. Boreman)

# ...................... ETM 

### Run Results Incorporating Thermal Model f-factors (J. 

### Boreman)

# ................. Incorporationof Sublethal Effects and Indirect

. 

### Mortality in Modeling Population-level Impacts 

### of Power-plant Entrainment 

### (W. Van Winkle 

### and S. 

### W. Christensen)

# ................... Page

D-1

E-l



### F-1

r-L G-1

/, 

### M-1

P-1

.-­

,--

### CHAPTER 

### I MATHEMATICALMETHOIDS USED 

### IN ESTIMATING CONDITIONAL

### ENTRAINMENTMORTALITY 

### RATES OF SIX

### HUDSON RIVER 

### FISH POPULATIONS

Joint Testhny

### of 30hn

### Boreman, 

### Ph.D. C. 

### Phillip

### Goodyear, Ph.D.

National Power Plant Team U.S. 

### Fish 

### and Wildlife Service 2929 PlymouthRoad 

### AnnArbor, Michigan 

### 48105

and


### Sigurd

### W. Christensen, Ph.D.

Environmental Sciences Division

Oak 

### Ridge National Laboratory

Oak 

### Ridge, Tennessee 37830 +


### PREPARE3 

### FOR 

### THE U.S. ENVXR0"SAL PROTECTION AGENCY, 

### REGION 

### I1

May 

### 1979

### 1-1



### 1. 

### INTRODUCTION

Ma hema ical

### me hods are used in

### this testimony for estimat-ng the conditional entrainmentmortality rates imposed by Hudson River power plants on six fishpopulations (striped bass,white perch, blueback herring/alewife, American shad,Atlantic tomcod, and bay anchovy). Conditional entrainmentmortality rates are estimated by using the Empirical TransportModel 

### (Em) developed by Boreman et al. 

### (1978). This reference, denoted herein asAppendix A, 

### is availableunder separate cover (see *'References Cited" section); it provides more complete detail on the mathematical basis and application 

### of the model. The purpose of this chapter is to present the mathematical formulationof the 

### ETM, as used in this exhibit, discuss the physical and biological input parameters that are necessary to use the model,
assess several assumptions implicit in the model, and compare the ETM 

### to the models used by the utilities to

### estimate conditional entrainment mortality rates.

2. 

### MATHEMATICAL 

### BASIS OF THE ETM



### The ETM 

### can 

### be used to estimate the conditionalentrainment mortality rate of fishpopulations inhabiting the HudsonRiver. The 

### ETM, which is essentiallyamathematical equation,relies on knowledge 

### of the morphometry 

### of the water body; power plant water withdrawal 

### flQw rates; the probability that an entrained organismwill survive plant passage; and the duration, distribution, and relative abundance of entrainable life stages. 

### A distinguishing feature of the ETM 

### is that the distributionand movement of entrainable organisms among regions of the water body is

### defined by 

### informationderived from field samples. By using such information, the ETM 

### avoids the difficulties often associated with hydrodynamic transport

### models, suchas 

### the Real-Time 

## _-_

~ __.____ 

### Life Cycle Model 

### (RTLCM) developed by Lawler, Matusky 

### and Skelly

- ­



### _ _ \ 

### Engineers 

### (LMS 1975), which rely on hydrodynamic principles and /



### equations to define organismmovement. Swartzman et al. 

### (1978) discuss-- 

### 1-2

problems associated with applying hydrodynamic models to biological data in order to assess power plant impacts. The ETM 

### formulation used in this testimony is a "Type 

### 11" ETM 

### (p. 11, Boreman et al. 1978), which 

### is applicablewhen time-dependent spatial distributiondata are not available 

### for each age group 

### of the selected fish population. This condition prevails when distribution data are categorized by life stage rather than calendarage. 

### As such, the observed spatial distributions over the entire entrainment period (the period of time during which entrainable life stages are present in the water body) 

### are averaged over all weeks for each life stage, with each week's contribution weighted by its relative abundance. The resultant parameter values can be used to calculate the conditional entrainment mortality rate as follows (p. 13, Boreman et al. 

### 1978):

where 

# 5 = 

### total conditional entrainment mortality rate,


### s = 

### week 

### of 

### the spawning period, 

### S = 

### total number of weeks in the spawning period, 

### R = 

### proportion of total eggs spawned that are spawned in

S 

### week 

### s, j = 

### age 0,1,2,...,3 (in weeks), 

### J = 

### oldest entrainable age, also the duration 

### of the entrainment interval (in weeks), 

### .t = 

### life stage I.,2,3,...,Ls 

### L = 

### total number 

### of entrainable life stages, 

### k = 

### region 1,2,...,K, 

### K = 

### total number of regionswithin the water body, Dke= 

### average proportion of the total standing crop of 

### life stage

## L individuals in region k 

### during the entrainment period, I--

.,

~

# - 1-3



### Es+j,kC instantaneous entrainment mortality rate constant 

### of life stage &individuals during week s+j 

### in region 

### k (units: per day), proportion of age j 

### individuals in life stage 

## C,and t= duration of themodel time step 

### (7 days). Calculation of 

### the instantaneous entrainment mortality rate 

### (E) requires specifying power plant flow rates, region volumes, and susceptibility of individuals within a region towithdrawal by power plants and subsequent mortality due to plant passage. These parameters
are expressed mathematically as follows:



# - 's+j,k 

### fs+j,ka 's+j,ka 

### Es+j,u 'k

where 

### Ps+j 

### ,k = 

### power plant water withdrawal 

### flow rate from region k during week s+j 

### (units: per day), 

# *s+j

## , ke= 

### fraction of life 

### stage 

## a. individuals entering the intake that are eventually killed by plant passage occurring during week s+j 

### in region 

### k, Ws+j

## , k& = 

### ratio 

### of the average intake concentration to average region concentration of life stage 

## t individuals during week s+j 

### in region k, and 

### Vk = 

### volume of 

### region k (assumed constant throughout the entrainment period). Figure 1-1 is a schematic diagram that illustrates operation 

### of the ETM. 

### Physical data necessary for the ETM 

### are the number of geographic regions within thewater body and their respective volumes, aswell as the power plant withdrawal flow rates from each region. Necessary bilogical data include the distribution of entrainable organisms, by age or life stage, among the water body regions; the ratio of average intake to

### average regional concentrations of 

### organisms (entrainment susceptibility factor, herein called the U-factor); the fraction of 

### organisms entering the intake that is eventually killed by plant

### 1-4

E 0

a W a

rl I H al & 3 M rl' 

### cr1-5



### passage (herein called the f-factor); and the'temporal distribution of 

### egg deposition or recruitment to the first vulnerable life stage. In addition, selection of 

### entrainment periods and entrainment intervals for each population is necessary. Subsequent chapters 

### of this testimony (Chapters 11-VII) present more detailed definitions of each input parameter variable used in the ETM, 

### aswell as derivations of 

### population-specific input parameter values.



### 3. 

### ASSUMPTIONS

### UNDERLYING 

### THE ETM



### Several assumptions are implicit in the 

### ETM. Violation of these assumptions may significantly reduce the accuracy of the estimate 

### of the conditional entrainment mortality rate. Often the direction of 

### the bias (whether the estimate is anoverestimate or underestimate of the true conditional entrainment mortality rate) is known, but the degree of 

### bias is 

### not. The first assumption, and probably the most critical one, is 

### that the data used 

### to establish spatial and temporal distributions are 

### accurate- Problems associated with gear bias, species and life stage identification, sample design, and data interpretation reduce overall accuracy of the ETM 

### estimate. However, such problems also reduce accuracy in estimates from any other methodology that incorporates distribution and vulnerability data derived from field samples, suchas the 

### RTLCM and empirical methods used by the utilities in exhibits 

### UT-3, UT-4, UT-6, 

### and UT-7. A 

### second assumption that is implicit in application of 

### the 

### ETM is 

### that organisms move instantaneously among regions of the water body between time steps and do not move among regions within each time step. As such, near-field (i.e., within-region) depletion 

### of organisms due to entrainment mortality within one region is 

### not offset by 

### movement 

### of other organisms into the depleted region during a given time step. If the organism distribution data are collected during power plant operation, the data base for both the river-wide distribution and the W-factors inherently reflects the near-field depletion and the degree to which it is offset by organism movement during the time step.

### 1-6



### If power plant effects are large enough in relation to organism



### . movement during the period 

### of data collection to have substantially altered the distribution patterns 

### of the organisms (i.e., through localized reduction in standing crops), the 

### ETM estimateswill be biased low. 

### In addition, if the ETM 

### is used to estimate conditional mortality rates for projected power plant flow conditions that are different from those conditions corresponding to the period 

### of data collection, further biases can be expected. The direction 

### of these further biases will depend onmany factors and can be controlled, to
some extent,'by making judicious choices for region size and length of the time intervals used in themodel. Another salient aspect of the ETM 

### methodology is the assumption that the organism distributionparameter values are based on field measurements 

### of the entire standing crop of each entrainable life stage. 

### If some members of 

### an entrainable life stage are located outside the area of sampling, then the ETM 

### will overestimate the conditional entrainment mortality rate for the entire population. Nevertheless, it will still reflect the mortality rate 

### of that portion 

### of the population that remainswithin the sampled area. A final assumption concerns.the uniformity of natural mortality within themodeled system. The "Type 11" ETM, 

### as used in this testimony, implicitly assumes that the natural mortality rate of 

### a given life stage is the same in all regions of the water body during the entire time that life stage is present within the entrainment period; that is, no differential natural mortality occurs among regions of 

### the water body. If differential naturalmortality does 

### occur, and it is measureable, then a 

### more generalized version of the ETM (Eq. 

### 5, Boreman et al. 1978) can be utilized to calculate the conditional

, ,I 

### entrainmentmortality rate. However, time- and age-dependent natural mortality rates dueing the entrainment periods for the 

### six fish populationswere not measured by the utilities' consultants or computed by US* 

### The "Type 11" 

### ETM also assumes that natural mortality rates are independent of population density. If 

### density-dependent mechanisms such as cannibalism, or density-dependent forms of 

### starvation or

### 1-7

predation are operative concurrentlywith reduction in numbers due to entrainment mortality, this assumptionwill not be realistic. Density-dependent mortality can be incorporated directly into ETM 

### estimates by allowing the natural mortality rate during a given time step to be a 

### function of 

### the population size at the beginning 

### of that time step, or incorporated indirectly by 

### multiplying the final ETM 

### estimate by 

### a coefficient that accounts forcompensatory capability of the population during the entrainment period. The former technique involves quantification

### of the density-dependent mortality function in space and time, which is 

### generally beyond the present state-of-the-art and has 

### not been undertaken 

### for tiudsonRiver fish stocks. The latter technique requires a prolonged series of appropriate stock and recruitment data (Christensen et al. 1977), which are 

### not available for the six Hudson River fish populations. Since both techniques are extremely difficult to quantify even approximately, the level of 

### confidence in the estimates would almost always be very 

### low. An 

### alternative to using either of these techniques when density-dependent mechanisms are 

### known or assumed to be operative during the entrainment period.is tomake 

### a judgment of 

### the significance of the effect based on the conditional entrainment mortality rate estimate from the 

### ETM.

# 1

4. COMPARISONOF 

### THE ETH 

### TO THE UTILITIES' EMPIRICAL MODELS



### In their direct testimony, the utilities presented two empirical methodologies for calculating the conditional entrainment mortality rates of selected fish populations inhabiting the Hudson River. One methodology, developed by Texas Instruments, Inc. 

### (TI) (p. 

### 2-VI-3, Exhibit UT-3), 

### compares the estimated number 

### of each entrainable life stage "cropped" by entrainment during a specified time interval to the standing crop of ichthyoplanktonpresent in the river ("adjusted" to account for incomplete recruitment of eggs to the entrainable population) during the same speciefied time interval. The second methodology, developed by LMS (pp. 

### 9.1B-5 to 9.1B-10, exhibits UT-6 and 

### UT-7) divides the 

### total number of organisms 

### entrained by a 

### power plant

### 1-8

by the average standing crop of entrainable organisms during the entrainment period.

4.1 

### TI METHODOLOGY The 

### TI methdology adjusts for the fact that, before all eggs are spawned, the entrainment mortality imposed by power plants is

### only experienced by that part of the population present in the river and not the entire population of entrainable organisms that will be present after all the eggs are spawned. This adjustment for unspawned individuals, however, 

### is not complemented by a necessary similar adjustment at the end of 

### the entrainment period which would allow recruitment to the 

### first non-entrainable life stage. The failure to
use this complementary adjustment could result in an overestimate of the conditional entrainment mortality rate. 

### As shown in subsequent chapters of 

### this exhibit, however, when the 

### TI methodology is applied, entrainment of all selected species is arbitrarily cut off 

### before recruitment to the first non-vulnerable life stage is fully realized. Because of this, entrainment mortality is not accounted for at all after the cutoff date. Whether this approach overestimates or underestimates the conditional entrainment mortality rate will depend
on the particular cutoff date used. The ETM, 

### by using a cohort approach, allows for an explicit temporal distribution of 

### recruitment to the first entrainable life stage similar to that used in the 

### TI methodology, as well as an explicit temporal distribution of recruitment to the first non-vulnerable life stage, which the 

### TI methodology lacks. An assumption underlying the 

### TI methodology is that, within each time interval during the entrainment period, the various entrainable life stages of a 

### given species 

### present in 

### the water body during that time interval have exactly the same distribution patterns (Tr. 10340). In calculating their entrainment ratio (ratio of 

### the number of organisms entrained to 

### the standing crop 

### in the river), TI combines organisms across life stages in both the 

### nirmerator and the denominator. Thus, in any given week, It is 

### the loss of the most I -

,”

### 1-9
numerically abundant organisms which willgovern the 

### size of the ratio. The fact that the older, and hence less abundant organisms which 

### are killed 

### are individuallymore valuable to the population is not properly accounted forinan equation as simplistic as equation 

### 2-VI-2 of Exhibit

### UT-3, unless allorganisms have the same distribution regardless of age, and power plant impacts are spread evenly among the different ages. The weakness 

### of 

### TI's approach 

### is best illustrated with a simple example involving a hypothetical population. Suppose spawning occurred evenly over a four-week period with one billion eggs spawned eachweek, and there 

### are three entralnable lifestages (egg, larva, and juvenile) inthe population, each lasting one week with a 90 percent mortality between each lifestage. In the fourthweek suppose that 

### 60 percent of thejuveniles were ina 

### power plant region and the power plant killed 20 percent of allindividuals inthat region. Simple arithmetic shows that the 

### loss of juveniles alone to this power plant represents a conditionalmortality 

### rate of 

### 0.03 tothe entire year-class. This number 

### is derived by considering that, since spawning 

### was evenly distributed through four weeks, one-fourth of the potential year-class 

### exists as juveniles inweek 

### 4, and 60 percent of these arewithin the power 

### plant region. therefore: Of this 

### 60 percent, 20 percent 

### are killed,

(0.25)(0.60)(0.20) = 

### 0.03,

or 3 percent of the year-class 

### isdestroyed asjuveniles inthisweek. Assume, however, that the power plant 

### is located downstream, while most spawning occurs upstream and only 10percent 

### of the eggs are found 

### inthe power plant region. For the sake of simplicity, alsoassume that 

### no larvae arekilled by the plant. Now it 

### ispossible toevaluate what 

### TI's equationwould calculate for that week. Ten percent of the one billion eggs are located within the.power. plant region; 20 percent of these, or 20million eggs, are killed. This represents an additional 0.5 percent loss tothe total potential

### 1-10

year-class (0.25 x 

### 0.10 x 

### 0.20 = 

### 0.005) and, between eggs and juveniles, 

### 3.5 percent of 

### the year-class has been killed. With 90 percent mortality between eggs and larvae and between larvae and juveniles, there will be 10million juveniles in this week (week 

### 4), 60 percent 

### (6million) are in the power plant region, and 

### 20 percent of 

### these (1.2 million) are killed. The numerator of TI's entrainment ratio is the sum of the eggs and juveniles that are killed: 

### 20 million eggs plus 1.2 million juveniles equals 

### 21.2 million organisms. The denominator of 

### TI's ratio is the total standing crop 

### of organisms 

### in the system: one billion eggs plus 100million larvae plus 10million juveniles, or 1.11 x 

### 109 

### organisms. The ratio 

### of these 

### sums 

### is 0.019. This ratio would form TI'S estimate 

### of the quantity mi in their equation 2-VI-2 in Exhibit UT-3, which is defined as the "probability 

### of death from entrainment during the ithinterval." 

### If the power plant operated only during that one week

## 'miwould also be equal to TI's quantity

# 3 in equation 2-VI-1 of 

### Exhibit UT-3, defined as the "conditionalmortality rate due to entrainment." Clearly, the estimate of 0.019 is a substantial underestimate of 

### the true impact during that week 

### (0.035). The cause of this difference is due to the different spatial distributions of 

### eggs and juveniles. Although the juveniles are a more importanf component of the actual loss because each juvenile has 

### a much higher survival value than each egg, their loss is masked by the loss 

### of the more abundant eggs. The estimate 

### of entrainment 

### is thus governed disproportionately by the distribution

### of eggs. The proper "conditional mortality rate due to 

### entrainment" cannot, in general, be obtained from equations 2-VI-1 and 2-VI-2 in Exhibit UT-3. The desired conditional probability of 

### entrainmentmortality is 

### defined as:

Ne 

## mTir (1-3). 0

### 1-11

where 

## 9= 

### conditional mortality rate due to entrainment, 

### N 5 

### the number of non-entrainable fish produced with the

e 

### power plant operating, and 

### N = 

### the number of non-entrainable fish produced without the

0 

### power plant operating. 

### In a situation where the probability 

### of entrainment mortality differs for organisms 

### of different ages, either due to different spatial distributions or different vulnerabilities to 

### the power plant, Equation 2-VI-1 inExhibit 

### UT-3 will only be correct if the individual
m

### i components in Equation 

### 2-VI-2 in Exhibit UT-3 

### have been calculated with proper weighting for the various life stages. That entire concept 

### ismissing from 

### TI'S "Empirical Entrainment Methodology", but not from the ETM. 

### The ETM 

### addresses this concept by handling the distribution pattern and entrainment susceptibility 

### of each life stage separatelyThe ETM 

### approach is 

### more realistic for two reasons: (1) life stages may have different vertical and longitudinal distribution characteristics, aswell as differential susceptibility to power plant induced mortality, and 

### (2) the total standing crops of the entrainable life stages that are present concurrently in 

### the water body may 

### vary by
as

### much as several orders of

### magnitude.



### 4.2 LMS METHODOLOGY 

### Since intake samples are used, the 

### LMS methodology implicitly
assumes that sampling efficiencies of gear used at power plant intakes
are equal to efficiencies of 

### gear used tomeasure rivervide densities of organisms. .Carpenter 

### (1979) and Barnthouse et al. (Chapter

### V) show that the intake sample densities are biased when compared to

### densities obtained by river sampling and that 

### direct comparisons reflect this bias. Consequently, the conditional entrainment mortality rates derived by 

### the 

### LMS methodology are biased estimates of the 

### true values. The extent to

### which the true values are underestimated or overestimated depends on the 

### degree of differential sampling efficiencies among 

### the gear.used at the power plant intakes and in the river.^.

1-12



### 4.3 ADDITIONAL CONSIDERATIONS Additional differences among the 

### ETM and utilities’ estimates

### of conditional entrainment mortality 

### rates 

### are due todifferent values used for the input parameters 

### in the various methodologies. Specifically, differences 

### exist for power plant withdrawal 

### flow rates (actual and projected)

# ,duration of the entrainment periods

# ,the fractions

### of organisms entering the power plant intakes that 

### are killed 

### by plant passage, and the relationshipsbetween intake densitiesand near-field densities. These differences are discussed as each input parameter value used inthe

### ETN analyses 

### is introduced.

### 1-13

REFERENCES CITED

Boreman, J., C. ?=

### Goodyear, and 

### S. 

### W. Christensen. 1978. An empirical transport model for evaluating entrainment 

### of aquatic organisms 

### by power plants. U.S. 

### Fish and Wildlife Serv. FWS/OBS-78/90. 67pp. Christensen, 

### S. 

### W., D. 

### L. DeAngelis, and 

### A. G. Clark. 1977. Development of a stock-progeny model 

### for assessing power plant effects on fish populations. pp. 

### 196-226. 

### INW. Van Winkle (ed.), Assessing the Effects of 

### Power-Plant-Induced Mortality on Fish Population. Pergamon Press, New York. 380 pp. Exhibit EPA-196. Carpenter, 

### E. J. 1979. Sampling ichthyoplankton. Exhibit UT-3. Supplement I to influence of 

### Indian Point Unit 

### 2 and other steam electric generating plants on the Rudson River estuary, with emphasis on striped bass and other fish populations. Edited by 

### J. T. McFadden and J. P. Lawler. Submitted to Consolidated Edison Company of New York, Inc. July 1977. Exhibit UT-4. 

### Influence of 

### Indian Point Unit 2 and other steam electric generating plants on the Hudson River estuary, with emphasis on the striped bass and other fish populations. Edited by 

### J. 

### T. McFadden. Prepared for Consolidated Edison Company of New

### York, Inc. July 1977. Exhibit UT-6. Roseton Generating Station. Near-field effects of oncethrough cooling system operation on Hudson River biota. Central Hudson Gas and Electric Corporation. July 1977. Exhibit UT-7. Bowline Point Generating Station. Near-field effcts of 

### once-through cooling system operation on Hudson River biota. Orange and Rockland Utilities, Inc. July 1977. Lawler,Matusky and Skelly, Engineers (LMS). 

### 1975. Report on development of a real-time two 

### dimensional model of the Hudson River striped bass population. Prepared under contract with Consolidated Edison Company of New 

### York, Inc. Swartzman, G. 

### L., 

### R. B. Deriso, and C. Cowan. 

### 1978. Comparison of 

### simulation models used in assessing the effects of 

### power-plant-induced mortality on fish populations. 

### U. 

### S. Nuclear Regulatory Commission. NUREG/CR-0474. 155 pp.

### CHAPTER 11 

### PHYSICAL INPUT PARAMETER 

### VALUES USED 

### TO ESTIMATE 

### CONDITIONAL

### ENTRAINMENTMORTALITY RATES FOR 

### SIX HUDSON 

### RIVER FISH POPULATIONS



### Testimony 

### of 

### John Boreman, 

### Ph.D.

~ 

### National Power 

### Plant 

### Team U.S. Fish

### and 

### Wildlife Service 

### 2929 Plymouth Road 

### Ann Arbor, Michigan 

### 48105

PREPARED 

### FOR THE 

### U.S. ENVIRONMENTAL PROTECTION AGENCY, 

### REGION I1



### May 

### 1979

### 1. INTRODUCTION

Use 

### of the Empirical Transport Model (ETM) 

### for estimating the conditionalentrainmentmortality rate of fish populations requires selection 

### of appropriate biological and physical input parameter values. Although thebiological input parameters vary depending on the particular fish population being examined, the physical input parameter values used in the ETM 

### are the same for all the populations. The physical input parameter values used 

### in the 

### ETM to estimate conditional entrainment mortality rates include water volumes of each region, power plant water withdrawal flow rates, and distribution

### of the power plant water withdrawal flow rates among regions.

2. REGION 

### VOLUMES
Region volumes used in the 

### E& are listed in Table 11-1. The regionswere chosen to coincidewith the stratified sampling scheme employed by 

### TI 

### in its Long 

### River, beach seine, fall shoals, 

### and bottom trawl surveys conducted during 1974 and 1975. The regions

### were defined 

### by 

### TI on the basis of morphometric characteristics such as depth, width, and extent 

### of shoals (areas 

### 5 6mdeep) (p. 

### 6.3, Exhibit UT-4).

The procedure 

### TI used to estimate region volumes is presented in the First Multiplant Report (p. 

### D-3, TI 1975). The volume of water in each of the three depth stratawithin each mile was calculated from the surface area and depths recorded on United States Geological Survey 

### (USGS) maps 

### of the river. Total surface area of 

### each miie segment was obtained with the use 

### of a polar planimeter. As 

### such, volume estimates rely on accuracy of 

### the USGS maps and the planimetric technique used on themaps. 

### In addition, the shorelineused in the

### USGS maps 

### represents conditionsat mean lowwater. Therefore, the depth soundingsused to estimate strata

### volumes are 

### 1-2 feet belowmean tidal depths, as noted 

### in the legend of the USGS 

### maps for the Hudson River. Since average river depth

### varies among regions, estimated volumes of 

### the regionswith

11-1-- 

### 11-2

Table 

### 11-1. Water 

### Volumes for the 

### 12 Longitudinal 

### Regions of 

### the 

### Hudson River Estuarya

Region 

### River Miles Volume 

### (m3)



### Yonkers 

### (YK) 

### 14-23 229,420,287 Tappan Zee 

### (TZ) 

### 24-33 321,811,465 Croton-Haverstraw 

### (CH) 34-38 147,736,754 Indian 

### Point (SP) 

### 39-46 208,336,266 West 

### Point 

### (WP) 47-55 207,455,769 Cornwall 

### (CW) 56-61 139,791,019 Poughkeepsie 

### (PK) 

### 62-76 298,133,444 Hyde 

### Park (HP) 

### 77-85 165,484,666 Kingston 

### (KG) 86-93 141,469,879 Saugerties 

### (SG) 94-106 176,295,711 Cat

### skill 

### (CK) 107-124 160,731,743 Albany 

### (AL) 125-140 71,149,105

a

### supplied to

### EPA by 

### Marcellus 

### (1978) _-

I

### 11-3


### shallow depths

### will 

### be more 

### biased by tidal conditions than deeper regions.



### 3. POWER PLANT

### WITHDRAWAL

### FLOW RATES

Several 

### sets of power plant

### water withdrawal 

### flov rates are used 

### in 

### the ETM analyses: histcrical (1971 and 1975) and projected. The

. . historical flow

### rates, based on daily

### rates supplied 

### to

### EPA by the utilities (Euggins 1977; Hutchisou 1977; Marcellus 1977)

# ,are listed 

### in tables 

### IX-2 and 

### 11-3 for 

### 1974 and 

### 1975, respectively. 

### The rates in these tables

### are expressed 

### as average dailywithdrawal flows for each 

### week.Projected flows 

### rates (Table 

### 11-4) were obtained from several
sources. The once-through and closed-cycle cooling

### water withdrawal 

### rates for Bowline, Indian Point units 

### 2 and 

### 3, and Roseton 

### are based 

### on the lifetime average flow conditions listed in

### tables A-1 through 

### A95 of Exhibit 

### UT-3 and Marcellus (1979). Projected flov

### rates 

### for Lovett units

### 4 and 

### 5 and Danskarumer units 3'and 

### 4 are the averages 

### of 1974 and 1975values, 

### as provided 

### by theutilities (Huggins 1977, Hutchison 1977; Marcellus 1977). 

### Lovett units 

### 103~Danskammer units 

### 1and 2, and Indian Point

### Unit 

### 1are not included 

### intheprojectionsbecause they 

### are expected tobe used very 

### little, if 

### at

### all, In the future 

### (p. 5-180, Barnthouse 

### et al. 1977).



### 4. 

### POWER 

### PLANT FLOW

### RATE DISTRIBUTIONS AMONG REGIONS

Due tothe tidalnature

### of theHudson 

### River estuary, thepower plants

### may withdraw

### water 

### from more than one region. Each power plant's 

### water withdrawal Is

### assumed tobe directlyrelated totheproportion 

### of thetidalcycle when 

### water "belonging" to a particular region 

### is in front

### of theplant. In theHudson 

### River, the average tidal excursion distance 

### is an estimated 13

### miles (p. VI-12, 

### TI 1975). 

### The totalWithdrawal 

### flow for each power plant 

### is, therefore, distributed among the regions 

### in the

### ETM according to thepropo-tionof the

### volume of the 

### 13mile

### 11-4

- 

### Table 11-2. Actual 1974 Water 

### Withdrawal Flow Rates, 

### Expressed 

### as TCM!day, of 

### Hudson River 

### Power Plants Based on 

### Average of 

### Daily FlowRates During Each Week

-



### Indian Point

### b

- 

### Interval Bowlinea Lovetta 

### Unit 1 Unit 

### 2 RosetonC DanskamerC



### 4122-4/28 1697 1226 1036 1482 1219 1004 4129-515 

### 1722 1223 1234 3124 894 1307 516-5112 1722 1223 710 2750 527 1131 5I13-5119 1613 1223 947 1832 359 1209 

### 5120-5/26 0 

### 1271 1638 3976 235 1341 51

### 27-6

### 12 0 

### 1174 1621 4629 489 1335 613-6/9 1898 1296 1638 4432 

### 373 

### 1134 6110-6/16 2492 1180 1640 3768 334 1013 6/ 17-6/23 344

### 7 1333 1633 3914 349 1165 6/24-6130 3155 1223 1414 4190 400 1145 

### 711-7

### 17 3236 1343 1511 4430 34

### 5 1368 718-7I14 2822 1285 892 4433 380 1409 7/15-7121 3445 1034 1646 3913 366 1477 7122-7/28 3445 1222 1646 3666 1168 1654 7

### /29-814 3445 1159 1646 1300 i203 1686 8/5-8/ 11 3183 1291 1532 3599 2084 1629 8

### /12-8

### 118 3445 1350 1646 4510 1556 1527 8

### /19-8I25 3639 1251 1646 4613 2238 1627 8

### 126-9

### I1 3565 1410 1646 4688 

### 2279 1573 912-918 3816 1233 1601 3839 1284 1513 919-9/15 3816 1250 1646 4200 604 1624 ' 

### 9/16-9/22 4112 1219 1646 4090 2190 1302 9123-9/29 4186 1301 1646 3927 2144 1325 9/30-10/6 4186 778 1370 1617 2027 1648 10/7-10/13 4186 696 1646 2009 1561 1688 10/14-10120 4186 939 1646 3913 175 1616 10/21-10127 369Q 998 1646 3913 0 

### 1435 10/27-1113 3567 939 1618 3609 537 1427

a

### supplied to 

### EPA by 

### Hutchison (1977) bsupplied to EPA 

### by 

### Marcellus 

### (1977) 'supplied to 

### EPA by 

### Huggins (1977)

### 11-5

Table 11-3. 

### Actual 1975Water Withdrawal 

### Flow Rates, 

### Expressed as 

### TCMIday, 

### of Hudson River 

### Power Plants

### Based on Average of 

### Daily Flow Rates 

### During EachWeek

Indian 

### Pointb 

### Interval Bowlinea Lovetta Unit 

### I Unit 

### 2 

### Roseton' 

### ~aiskam==~

312-318 2216 94

### 9 740 1272 2278 452 319-3115 2802 839 740 752 2278 452 3/16-3122 2802 758 740 

### 7

### 91 2279 456 3/23-3129 2802 039 740 983 

### 2298 457 3130-4 

### 15 2802 821 740 1312 2279 452 4

### /6-4

### /12 2802 788 740 2375 2279 534 4/13-4119 2802 684 704 2938 2296 452 4120-4 I26 2802 

### 655 953 3420 2279 454 4127-513 2986 

### 655 33 3584 2565 789 

### 5/4-5/10 2165 711 

### 588 3699 2712 840 5/11-5117 1447 760 796 3913 2657 882 

### 5/18-5I24 1401 1367 312 

### 3915 3058 880 5/25-5131 1401 1223 185 4

### 117 3004 1076 611-617 2368 1223 446 

### 4492 2953 1427 6/8-6114 2802 1197 713 4582 2274 1481 6115-6I21 2802 

### 1081 556 4714 2474 1551 6/22-6128 2848 1278 4

### 50 4703 3058 

### 1656 6129-7

### /5 2802 1060 187 4453 2824 1619 716-7112 2802 1151 222 4643 2824 1292 7/13-7/19 2802 1167 107 4688 2932 1424 7120-7126 3353 1253 565 4659 3011 1487 7127-812 3429 1359 867 1756 

### 3058 1519 813-819 3445 1250 705 11 7 

### 2722 1634 8110-8

### I16 3445 1205 .32 4033 2125 1544 8/17-8123 3362 1221 25 4390 2324 1547 

### 8124-8

### /3

### 0 3356 1216 270 4645 2279 1590 8131-9/6 3445 1223 25 4715 2279 1494 917-9113 3364 1235 25 3897 2279 1506 

### 9114-9120 3445 6

### 34 25 3979 1928 1561 9/21-9127 3445 589 25 3925 2283 1536 9128-10I4 3323 94

### 3 55 

### 3770 2427 1484 

### 1015-10111 1723 1150 112 3925 2574 1177 10112-10118 1723 941 112 2995 2528 1352 10/19-10125 1565 955 111 

### 872 2309 1548 10126-11/1 1608 1076 112 

### 1789 2279 1307
"supplied to 

### EPA by 

### Hutchison (1977) bsupplied to 

### EPA by Marcellus (1977) 'supplied to

### EPA by Huggins 

### (1977)

### 11-6

Table 

### 11-4. ProjectedWater Withdrawal Flow Rates, Expressed as 

### TCM/day, 

### of Hudson River Power Plants

Indian Point' Interval



### . 

### Bowlines Lovettb Units 

### 2 li 

### 3

, 

### Rosetond Danskannnerb April 1543 2061 482 (48) 

### (78) 

### hY 2524 1061 6470 2644 720 

### (78) (434) (70) 

### June 3816 1164 6442 3034 945 (87) (439) (74) July 4186 

### 1133 9130 3482 

### 1062 

### (87) (543) (87) 

### August 4186 1176 9123 3482 1237 (87) 

### (545) (87) September 3657 1030 7919 2730 1179 

### (83) (542) (38) 

### October 2544 903 6971 1780 

### 1084 

### 177) (484) (34

### 1

afrom tables A-4 and A-5 

### of Exhibit 

### UT-3 and 

### Marcellus (1979) baverage of 1974 and 1975values for units 

### expected to be 

### inoperation 

### cfrom tables A-2 and A-3 

### of Exhibit 

### UT-3 and 

### Marcellus (1979) dfrom Table 

### A-1 

### ofExhibit 

### UT-3 and Marcellus (1979)

Numbers 

### in parentheses 

### are average monthly flows with closed-cycle coolingconditions ,..-11-7

segment surrounding eachpower plant intake that failswithineach region. These proportions 

### are listed inTable 11-5.

5. REFERENCE 

### CITED
Barnthouse, L. 

### W., 

### J. B. Cannon, 

### S. G. Christensen, A. 

### H. Eraslan, 

### J. L 

### Harris, 

### K. R. Kim, 

### M. E. Laverne, 

### H. A. McLain, B. 

### D. Murphy, 

### R. 3. Raridon, 

### T. 

### H. Row, 

### R. D. Sharp, and W. Van Winkle. 1977. A 

### selective analysisof power plant operation.onthe Hudson River with emphasis on the Bowline Point Generating Station. Oak Ridge National Laboratory. ORNL/TM-5877/V2. Exhibit

### UT-3. Supplement 

### I toinfluence of Indian Point Unit 2 and other 

### steam 

### electric generating plants on the Hudson River estuary, withemphasis 

### on striped bass and other fish populations. Edited by J. 

### T. McFadden and J. P. 

### Lawler. Submitted 

### to Consolidated Edison Company 

### of New York, Inc. 

### July 1977- Exhibit

### UT-4. Influence 

### of Indian Point Unit 2 and other 

### steam electric generatingplants on the Hudson River estuary, with emphasis on striped bass and other fishpopulations. Edited by 

### J. ToMcFadden. Prepared for Consolidated Edison Company 

### of New York, 

### Inc. 1977. Huggins, To 1977 (pers. corn.). 

### Letter toHenry Gluckstern (EPA) dated October 31, 1977. Hutchison, 

### J. E. 1977 (pers. comm.). 

### (FWS) dated October 

### 27, 1977. Marcellus, 

### K. 

### L. 1977 (pers. corn.). dated November 

### 10, 1977. Marcellus, 

### KO I..1978 (pers. comm.). dated April 

### 12, 1978. Marcellus, 

### K. i. 1979 (pers. 

### comm.). dated March 2, 1979. Letter toC. Phillip Goodyear



### Letter toHenry Gluckstern 

### (EPA)

Letter to Henry Gluckstern (EPA)

Letter toHenry Gluckstern 

### (EPA)

Texas Instruments, Inc. 

### (TI) 1975. Firstannual report forthe multiplant impact study of the Hudson River Estuary. Vols. 1-11. Prepared under contract with ConsolidatedEdison Company of New York, Inc.

### 11-8

Table 

### 11-5. Proportion of Power 

### Plant CoolingWater Flows Withdrawn 

### from each Longitudinal Region of the 

### Hudson 

### Rivera

Power Plant Region 

### Bowline Lovett Indian Point Roseton 

### Danskammer _-

YK


### TZ 0.271 0 

### 0 0 

### 0 

### CH 0.358 

### 0.369 0.298 

### 0 0 IP 

### 0.371 0.549 0.562 

### 0 

### 0
- - WP 

### 0 0.082 0.140 

### 0 0



### __ 

## cw 0 0 

### 0 0.273 0.196


### PK 0 

### 0 0 

### 0.727 0.804


### HP 0 0 

### 0 0 

### 0
KG 

### 0 0 0 0 

### 0
SG 0 

### 0 0 0 0 

### CK 0 

### 0 0 0 

### 0


### AL 0 0 

### 0 0 0



### - ,~



### abased 

### on a 13

### mile dailytidal excursionand river 

### volume data
r­



### (Table D-2, TI 

### 1975)

### CHAPTER 

### I11 SPATIAL 

### DISTRIBUTIONS

### OF ENTRAZNABLE LIFE 

### STAGES OF SIX HUTSON RIVER 

### FISH POPULATIONS

Testimony 

### of John 

### Boreman, 

### Ph.D.

National Power Plant Team 

### U.S. Fish and Wildlife Service 2929 Plymouth 

### Road 

### Ann Arbor, Michigan 48105



### PREPARE3

### FOR 

### THE U.S. 

### ENVIRONMENTAL PROTECTIONAGENCY, 

### REGION 11

May 

### 1979

### 1 

### INTRODUCTION
Susceptibilityof an individual organism to withdrawal by power plants dependson the locationof that individualwithin the water body in relationto the location

### of power plant intakes. This relationship may change'duringthe individual's entrainable life stages as the individualchanges itspositionwithin the water body either 

### by movement
or passive transport. The Empirical TransportModel (ETM), 

### used in this exhibit to estimate the conditionalentrainmentmortality ratesof six 

### populations inhabiting the Hudson River estuary,accounts for differential susceptibilityof individualsdue to their locationwithin the water body and movement of these individualsas they age. The parameter 

### in the ETM that accomplishesthis is the 

### Dparameter (equation 1-1 

### in Chapter I of this exhibit). The

### D parameter is the proportion of the total standing crop of a 

### life stage that ispresent within a specified region of the water body.

The 

### "Type 11" 

### ETM used in this exhibit implicitlyassumes that the distributionsof each life stagemay be different,but that the distribution 

### ofa given life stage remains constant throughout the period of occurrence 

### of that life stage in the water body. To meet this assumption, the distributionof 

### each life stage representsan average of 

### the distributionsof that life stage recorded during the period that it 

### is present in the water body. The average is weighted by 

### the relative abundance represented by 

### each distribution 

### to assure that the distributionsof 

### most members 

### of a life stage influence the resultant average to 

### the greatest extent.

2. 

### DERIVATION

### OF 

### PARAMETERVALUES

Egg and larval life stage distributionswere derived from Texas Instruments, Inc. (TI) Long River Survey data. The TI sampling program was designed to 

### sample ichthyoplanktonin

### most of 

### the Hudson River (RM 

### 14-140). Life stage distributions 

### of entrainable juvenileswere derived from 

### either the Long 

### River Survey data or the Long 

### River Survey

111-1

### 111-2

data used in conjunction with 

### TI beach seine data, depending on 

### the lateral and vertical distributions 

### of juveniles that are characteristic for the species of concern.

Average regional density data for each entrainable life stage of 

### the selected fish populations, based on 

### 1974 and 1975 Long River surveys, were provided to 

### EPA and its consultants by the utilities (Marcellus 

### 1977, 1978, 1979). Only 

### 1974data were provided for the tiudsonRiver population of 

### American shad. A description of 

### the methodology used by 

### TI to calculate regional densities of each life stage of 

### the selected populations is in the First Annual Multiplant Report (p.D-5, Vol. 11, 

### TI 1975).

The proportional distribution of 

### a given life stage among regions
was derived by multiplying the density of that life stage in each region during a specified time interval (sample week) by the respective region volume. The resultant weekly standing crops in each regionwere divided by the estimated weekly river-wide standing crop to derive the proportion of the estimated river-wide standing crop present in each region during that week. These proportions were then averaged across allweeks that life stage was present in Long River Survey collections, weighting by the river-wide standing crops estimated for the respective weeks.

For all fishpopulations except the Atlantic tomcod, which is 

### a demersal species (Boreman 1979), the spatial distributions of 

### entrainable juveniles were derived by adding the average weekly standing crop in each region, as estimated by the Long River Survey, to 

### the average weekly shorezone standing crop, as 

### estimated by the beach seine survey. Incorporation of 

### beach seine data into 

### the calculation of 

### relative distributions for themore pelagic populations allows recognition of 

### organism movement into the shorezone areas of the estuary during the juvenile life stage. As stated in the testimony on life histories (Boreman 1979), the relatively few juvenile tomcod caught in beach seines implies little movement of 

### this species into the shorezone during its 

### entrainment period. ,-.

, .

### 111-3

The resultant regional standing crop sums 

### were divided by the sum of 

### the average river-wide standing crop plus the average total shorezone standing crops to 

### derive the proportion of the entrainable juvenile life stage present within each region. 

### The use of average weekly standing crops avoided the problems 

### of non-comparability 

### of data bases associated with the several non-overlapping sampling weeks of the 

### two surveys. Beach seine data sometimes extended several weeks past the finalweek 

### of Long 

### River Survey data collection.
Standing crops estimated with the beach seine data were derived by multiplying the average regional catch per tow during a specified sample week by the average area swept by a tow. This product was divided by

### IT, the shorezone surface area of the specified region (Table 

### D-3, Vol. 

### TI 1975). The samemethod 

### of calculating shore zone standing crops was used by the utilities' consultants (p. 

### D-9, Val. 

### 11, TI 1975). The resulting average regional shorezone standing crops were then averaged
across sampleweeks, weighted by the total shorezone standing crop present in the river during each respective sampleweek.

Because of problems associated with gear avoidance, the standing .cropsderived by the above methodologies should not be regarded as absolute estimates 

### of the true regional 

### or river-wide standing crops. Rather, as used in the ETM, they are considered as direct indices of the true standing crops, necessitating an assumption that sampling gear catch efficienciesare equal in all regions of the water body. Factors that will affect the validity of 

### this assumption are, for themost part, abiotic. The factors include salinity, tidal-induced motion, water temperature, and water body 

### morphometry (Table 

### 2, Bowles et al. 1978). However, no data are available that relate the influence

### of such factors present 

### in the Hudson River to sampling gear catch efficiencies.



### 3. PARAMETER

### VALUES FOR SELECTED 

### FISH POPULATIONS

Average entrainable life stage distributionsused in tk.? ETM 

### to estimate the conditionalentrainment mortality rates of selected fish

### 111-4


### populations are listed in tables 111-1 to 

### 111-6 for striped bass, white perch, Alosa spp. (bluebackherring and alewife), American shad, Atlantic tomcod, and bay anchovy, respectively. Table III-7 lists the dates 

### of the 

### TI beach seine data collections used in estimating the distribution 

### of entrainable juveniles

### of all populations except Atlantic tomcod. Only day beach seine sampleswere used because night samples did not encompass the entire estuary.

r "I As 

### stated previously, the relatively low catch 

### of Atlantic tomcod in beach seines coupled with the epibenthic characteristicsof 

### the juvenilc life stage of this speciesprecluded use 

### of beach seine data in the derivation 

### of the spatial distribution of that life stage. The time intervals of beach seine data collectionsused to 

### estimate the distributioi 

### of entrainable juveniles

### of the other fish populations were chosen to represent the period when that life stagewas present in the Hudson River. This interval extended more than 

### severalweeks past the last date of Long 

### River Survey data collection only for the bay anchovy population, which essentially remains in the entrainable size range until maturity.



### 4. REFERENCES CITED

I-" 

### Boreman, 

### J. 

### 1979. Life histories of seven fish species that inhabit the Hudson River estuary. 94pp. Exhibit EPA 

### -198. Bowles, 

### R. R., J. 

### V. Merriner, and J. 

### Boreman. Factors affecting accuracy of 

### ichthyoplanicton samples used 

### in power plant-entrainment studies. U.S. 

### Fish and Wildl. Serv. FWS/OBS-76/20.7. 12 pp. Marcellus, 

### K. L. 1977 (pers. comm.). dated November 

### 30, 1977. Marcellus, 

### K. 

### L. 1978 (pers. comm.). dated Jme

### 23, 1978. Marcellus, K. 

### L. 1979 (pers. corn.). dated 

### February 21, 

### 1979. Texas 

### Instruments, Inc. 

### (TI). 1975. multiplant impact study of the 

### Hudson River estuary. ,-.. 

### Letter to 

### Henry Gluckstern 

### (EPA)

Letter to 

### Henry Gluckstern 

### (EPA)

Letter to Henry Gluckstern 

### (EPA) -_


### First annual report for the Vols. 11. 

### Prepared under contract with Consolidated Edison Company of 

### New York, Inc.

-7

### 111-5

Table 

### 111-1. Proportional Distributions, Expressed 

### asPercentages, 

### of

Entrainable 

### Ltfe Stages of'StripedBass 

### as

### Used in the 

### ETM Analyses

Yolksac Post 

### yolksac ' 

### Entrainable Year Region Egga larvaa larvaa juvenileb



### 10 

### 07 1974 YII 

### 0 0.14 

### 0.07 0.62 

### TZ 0.09 2.65 3.84 28.41 

### CH 18.20 6.72 23.10 

### XP 23.97 11.43 23.87 

### 5.01 WP 36.54 10.39 21.71 2.72 

## cw 2.63 22.12 12.28 11.38 

### PK 4.12 35. 30 18 0

### 70 2.74 

### HP 3.99 5.45 4.04 2.52 KG 5.85 1.40 6.47 9.43 SG 

### 1.88 0.73 1.26 4.60 

### CK 2.55 0.29 1.03 7.40 

### At 0.18 0.'03 

### 0.01 2.07 1975 

### YK 0 0.05 0.51 2.05 

### Ti? 0.32 4.34 2.24 32.27 

### CH 6.43 9.97 9.31 26.84 

### IP 35.71 23.02 34.46 9.13 

### WP 38.24 23.62 20.19 2.05 

## cw 9.22 

### 11.12 10.99 6.64 

### PK 4.99 12.82 13.65 8.59 

### HP 2.36 9.75 3.98 2.53 KG 0.48 2.38 3.39 3.73 SG 0.98 

### 2.83 0.82 4.24 CK 

### 1.19 

### 0.09 0.46 1.72 

### AL 0.08 0.01 

### 0 0.21

a

### from TI 

### Long River Survey data (Marcellus 

### 1977) bfrom Tf Long River and 

### beach seine survey data 

### (Marcellus1977)

### 111-6

--\



### Table 

### 111-2. Proportlonal Distributions, Expressed as Percentages, 

### of Entrainable Life Stages 

### of White Perch 

### as Used 

### in the ETM

### Analyses

~ _ _ ~~ ~ -~__ 

### Yolksac 

### Post yolksac Entrainablc Year Region 

# ma larvaa larvaa 

### jwenlleb '­

,--, 

### 1974 YK 

### 0 0.06 0.17 

### 0 

### TZ 8.07 17.56 4.38 2.76 

### c�I IP 47.03 1.37 12.36 2.89 3.58 15.50 1.18 2.02 .,- -. wp 

### 5.18 6.02 12 34 1.57 

## cw 4.10 4.24 10.19 3.85 

### PK 3.91 6.99 19.32 11.89 HP 

### 0.63 13.09 13.66 ._ 

### 8.24 

### KG 1.53 13.40 15.34 15.98 

### SG 7.91 14.85 3.12 42.25 

### CK 4.47 6.93 2.29 6.89 

### AL 15.80 1'. 61 0.11 

### 3.37 1975 

### YR 0 0.22 0.37 0.31 

### TZ 21.01 25.03 6.47 3.70 

## w 2.75 7.67 5.60 2.85 IP 

### 2.48 6.21 13.93 3.23 

### WP 4.58 5.55 9.71 4.91 

## cw 6.88 4.71 

### 7.17 7.48 PK 

### 25.66 9.87 13.91 16.30 

### HP 1.87 11.19 14.31 24.50 

### KG 5.58 5.40 9.17 17.17 

### SG 11.84 9.78 12.79 13.i8 

### CK 16.19 10.05 6.35 5.67 

### AL 1.16 4.32 0.22 0.71

afrom 

### TI Long 

### River Survey data (Marcellus 

### 1977) bfrom 

### TI Long River and beach seine 

### survey data (Marcellus 

### 1977)

### 111-7

Table 

### 111-3. ProportionalDistributions, Expressed as 

### Percentages, 

### of Entrainable Llfe Stage 

### of Alosa spp. 

### (Blueback Herring and 

### Alewife) as Used in

### the 

### ETM Analyses



### Year Region Egga Yolksac 

### larvaa 

### Post yolksac 

### larvaa Entrainable j

### uvenileb

1974 YK 

### 0 0 0.02 

### 0.15 

### TZ 0 

### 0.10 0.49 0.20 

### CH 0 0.17 

### 0.47 1.42 

### IP 0.02 0.03 1.52 0.08 

### UP 0.02 0.34 1.79 5.22 

## cw 0.01 

### 0.18 4.24 7.61 

### PR 0.22 9.39 9.11 7.48 

### HP 0.62 2.36 7.94 6.34

.

### KG 0.79 8.91 21.22 28.29 

### SG 3.93 20.27 26.98 3.45 

### CK 5.29 21.71 

### 17.09 34.66 

### AL 89.10 36.54 9.13 5.10 

### 1975 

### YK 0 0 

### 0.02 

### 0 

### TZ 0 0.06 

### 0.22 

### 0 

### CH 0 

### 0.10 

### 0.19 

### 0 

### IP 0.04 0.63 1.21 0.12 

### WP 1.32 1.61 

### 5.51 3.37 

## cw 1.36 3.19 8.41 7.55 

### PK 18.43 4.33 6.67 7.91 

### HP 7.08 2.23 8.60 4.28 

### KG 4.67 8.82 

### 10.91 24.30 

### SG 29.07 13.76 24.77 13.48 

### CK 23.34 29.40 24.96 

### 38.71 

### AL 14.69 35.87 8.53 0.28



### a

### from 

### TI Long River Survey data (Marcellus 1977) bfrom TILong River 

### and beach seine survey 

### data (Marcellus 1977, 1979)

### 111-8



### -. Table 

### 111-4. Proportiona Distributions, Expressed asPercentages, 

### of

Entrainable 

### Life Stages 

### of American 

### Shad as Used 

### inthe 

### ETM Analyses

Yolksac 

### Post 

### yolksac Year 

### Region Egga larva” 

### larvaa

~ 1974 YK 

### 0 0 0 

### TZ 0 

### 0 0.37 

## ai 0 0 0.28 

### IP 0.06 

### 0 0.54 

### WP 0 

### 0.71 0.62 

## cw 0 0.60 1.01 

### PK 2.35 10.95 5.70 

### HP 1.86 

### 7.05 9.81 

### KG 5.71 19.83 27.03 

### SG 60.25 41.22 36.36 

### CK 29.50 19.64 16.84 AL4 0.27 

### b 1.44



### afrom TI Long River Survey data (Marcellus 1977) -* 

### Entrainable

juvenileb

? -\

2.68 11.90 + ­

9.95 9.14 10.71 14.08 19.99

6.95

F 5.73 3.01 3.08 2.78



### bfrom TI Long 

### River and beach 

### seine survey data (Marcellus 1977)

### 111-9

Table 111-5. 

### Proportional Distributions, Expressed as Percentages, of 

### Entrainable Life Stages

### of Atlantic 

### Tomcod asUsed in

### the ETM 

### Analyses

Yolksac Post yolksac Entrainable Year Region Egga 

### lamab larvab juvenileb



### 1975 

### YK 15.02 58.81 

### 42.20 

### TZ 29.74 33.87 34.18 

### CH 28.64 4.89 6.68 

### IP 12.22 2.03 8.78 

### WP 8.36 

### 0.27 5.16 

## cw 2.83 0.09 1.56 PK 3.19 

### 0.04 1.20 

### HP 0 

### 0 0.13 

### KG 0 0 

### 0.02 SG 0 0 0.01 

### CK 0 0 0 

### AL 0 0 0



### data available bfrom TI 

### Long River Survey data (Marcellus 1977)r--. 

### 111-10



### Table 111-6. 

### Proportional

### Distributions, Expressed as 

### Percentages, 

### of Entrainable Life Stages 

### of Bay 

### Anchovy as 

### Used 

### in the 

### ETM Analyses -\

rl

I-



## _ - Yolksac Year 

### Region Egga 

### larvaa

1974 

### YK TZ CH 

### IP WP 

# cw PK HP KG SG 

### CK 

### AL 1975 YK 

### TZ CH IP 

### WP 

## cw PK HP KG 

### SG 

### CK AL 70.72 23.03 21.19 68.00

4.62 8.97 2.80 0 

### 0.67 

### 0

0 0 0 0 0 0 

### 0 0 0 0 0 0 0 0 

### 49.24 

### 0 40.13 74.27

3.98 25.73 6.40 0 

### 0.25 0

0 0 

### 0, 0 0 0 

### 0 0 

### 0 0 

### 0 0 

### 0 

### 0 

### Post yolksac 

### larvaa

14.97 42.49 24-68 15.53

1.34 0.58 0.23 0.03 0.03 0.01 0.01

0 

### 18.01 37.20 14-67 16.93 10.42

2.43 0.28 0.03 0.01 0.02

.



### 0 0 

### Entrainable j

### uvenileb

9.25 35.04 38.71 10.60

1^. 

### 04 

### 5.13 0.10 0.02 

### 0.07 0.01 0.03



### 0 16.84 50.22 12.99

2.93 6.38 9.99 0.48 

### 0.02 0.09 0.06

0 0

a

### from

### TI Long River 

### Survey data 

### (Marcellus1978) bfrom 

### TI Long 

### River and 

### beach seine survey data 

### (Marcellus 

### 1978)

### 111-11

Table 

### 111-7. Dates 

### of 

### TI Beach Seine Collections (Day Only)a Used 

### in Estimating the Proportional Distributions 

### of the Entrainable Life Stages 

### of Selected Hudson 

### River Fish Populations

Popul

### at

### ion

Striped 

### bass

White perch



### Alosa spp.

### b



# -

American shad 

### Bay anchovy 

### 1974 1975 1974 1975 1974 1975 1974 1974 1975 Dates



### 6/16

# - 8/10 6/15

# - 8/9 

### 6/30

# - 8/10 6/15

# - 8/9

6/1

# - 819 6/15

# - 819

612

# - 8/10 7/13

# - 10/18 7/13

# - 11/1

=provided to

### EPA by Marcellus 

### (1977, 1979) bBlueback herring and alewife 

### combined

### CHAPTER IV DURATIONS 

### OF 

### THE ENTRAINMENT 

### INTERVALS AND 

### ENTRAI"T 

### PERIODS 

### FOR SIX

HUDSON RIVER FISH POPULATIONS

Testimony of John Boreman, Ph.D.

National Power Plant Team U.S. 

### Fishand Wildlife Service 2929 Plymouth 

### Road Ann 

### Arbor, Michigan 

### 48105

PREPARED 

### FOR 

### THE U.S. ENVIRONMENTAL,PROTECTION AGENCY, REGION I1



### May 1979

### 1. 

### INTRODUCTION

The total period during which entrainable organisms are present within thewater body is termed the entrainment period. This period 

### is the combination 

### of the spawning period (period of egg deposition) 

### and the average 

### amount of time 

### an individual takes to grow through its eatrainable life stages. 

### The latterperiod 

### of time is termed the entrainment interval.



### The vulnerability 

### of an entrainable life stage of

### a given fish population to 

### entrainment mortality depends, among other factors, on the duration of that life stage and the calendar time period during which members 

### of that life stage are present in the water body. 

### A life stage may 

### be present in thewater body much longer than its specified duration. 

### This phenomenon occurswhen spawning is not instantaneous, but 

### is spread
over an extended period. 

### If physical conditions within thewater body, such as power plant water withdrawal flow rates, vary during the entrainment period, then individuals of a given life stagemay experience different entrainment 

### mortality rates depending 

### onwhen during the entrainment period they were spawned.

The Empirical Transport 'Model 

### (ETM) handles the phenomenon 

### of extended spawning by tracking the entrainment mortality 

### of each cohort through its entrainment interval. A 

### cohort is defined as 

### a group of individuals spawned during the samemodel time step (week). The total conditional entrainment mortality rate is the combined conditional entrainment mortality rates of the cohorts,weighted for the fraction of 

### the .totalnumber 

### of individuals spawned during the entrainment period that each cohort represents.

2. 

### DERIVATION OF PARAMETERVALUES



### Average durations 

### of entrainable life stages 

### of selected fish populations inhabiting the 

### Hudson River used in the 

### ETM were derived



### IV-1

### IV-2

from literature sources and field data, as presented in the testimony on life histories 

### of the populations (Boreman 1979). When a range 

### of average life stage durations 

### for a population was presented 

### in the 

### life histories testimony, theminimum value was used 

### in the 

### ETM. 

### If these minimum values are below the actual average life stage durations, then the 

### ETM willunderestimate the conditional entrainment mortality rate 

### of that life stage (with 

### all other input variables held constant). The average entrainment interval 

### for each fish population is the 

### sum of the average durations 

### of each entrainable life stage.



### In the 

### ETM analyses, the average entrainment interval 

### is considered constant throughout the entrainment period for each population. Use 

### of
an average entrainment interval in the ETM 

### underestimates the entrainment intervals of the early cohorts and overestimates the entrainment intervals 

### of the later cohorts In each fish population modeled. 

### If Conditions that affect entrainmentmortality vary 

### during the entrainment period, then use of 

### an average entrainment interval may lead to an overestimate
or Underestimate of the true conditional mortality rate, depending 

### on hov the conditions vary. However, only the egg (and in the case 

### of striped bass the yolksac larval) life stage durations can be directly related to a physical condition in thewater body (water temperature); durations 

### of other life stages in relation to physical 

### and biological conditionswithin thewater body cannot be quantified due to lack of sufffcient data. Therefore, average life stage durations are used for the older life stages and, as shown 

### in the testimony on life histories (Boreman 1979), variation in egg and yolksac larval life stage durations from average values is relatively small (generally less than one day).

The entrainment period for each selected fish population, as it 

### is used 

### in the ETM 

### analyses, begins when the first entrainable life stage appears in field samples and ends when the last cohort has reached the first non-entrainable life stage. 

# ".

### IV-3

The spawning period �or 

### a fishpopulation, as used in the ETM, 

### is the number of weeks that eggs of 

### that populationwere present 

### in the Texas Instruments, Inc. (TI) Long River Survey samples for 

### a given year. The proportion 

### of eggs spawned during each week of the spawning period, and hence the proportion of the initialnumber of individuals represented 

### by each cohort, is assumed equivalent to the estimated standing crop of eggs for that week divided by 

### the estimated total standing crop of eggs summed over all weeks of the spawning period for that year. This method 

### of calculating the temporal distribution of 

### egg deposition does not work if sampling was 

### conducted mare than once per week (which it WBS 

### not) or if the egg incubationperiod for 

### a given population 

### is longer than one week. Of the five fish populations for which a spawning period is used in the 

### ETM analyses (tomcod egg entrainment 

### is not calculated), the average egg incubationperiods are equal to or less than one week, although

### Alosa spp. may have an egg incubation period 

### as long 

### as 10 days 

### very early in the season (Table 14, Boreman 1979).



### 3. PARAMETERVALUES 

### FOR 

### SELECTED 

### FISH POPULATIONS

Durations of the entrainable life stages 

### of selected fish populations inhabiting the Hudson River are presented in Table IV-1. The valces for each populationwere derived in the testimony on life histories (Boreman 1979) and represent average durations for the time periods when those life stageswere present in the Hudson River. Where possible, values
are specified separately 

### for 1974 and 1975.

The 1974 and 1975 parameter values used in the ETM 

### analyses for durations of the spawning periods and temporal deposition of eggs 

### of 

### the selected fish populations are listed in Tables 

### IV-2 to 

### IV-6 

### for striped bass, white perch, Alosa 

### spp. 

### (bluebackherring and alewife), American shad, and bay anchovy. Since the 1974 Long River Survey began too 

### late
to 

### sampleAtlantic tomcod yolksac larvae, only 1975 data were used. The

. ETM 

### analyses assume 

### all Atlantic tomcod were 

### recruited to 

### the yolksac larval life stgge before the first week 

### of sampling in 1975. The peak

### IV-4



# 5 XO 

### ms mu 

# em

d 0 =A U rd 4 

### a 

### a 0 EA hI 4 

## f

uu



### m N. d

In

.N N

U c d

.. v urn m hb m m 

# E drl GI c, Q

a M Q U m 01 m yc bo 4 bo J w rld 00 NN uu mm uu I-h

I
. la0 IN Id Id

hl U N m

UI 4 1 Wl 0 1 N N \D

n a\ h m d W

U e 0 &I u.c C 0) & ad m 0 Y 01 (d

### IV-5



### Table IV-2. 

### Proportion 

### of Striped 

### Bass Eggs 

### Spawned Eagh 

### Week During 1974 

### and 1975 as 

### Used 

### in the 

### ETM Analyses

Pear 

### Week

. 

### Proportion

1974 4/23

# - 515 0.0025 516

# - 5/12 0.1226 5/13

# - 5/19 0.4189 5/20

# - 5/26 0.3875



### 6/3

# - 6/9 6/10

# - 6/16 6/17

# - 6/23 6/24

# - 6/30 0.0047 0.0046 0.0034 0.0017 1975 5/11

# - 5/17



# - 0.0309 5/18 5/24 5/25

# - 5/31

611

# - 617 0.4952 0.4115 0.0470 

### 6/8

# - 6/14



# - 0.0008 6/15 6/21 6/22

# - 6/28 0.0052 0.0094 5/27

# - 6/2 0,0541



### afrom Table .2

### in

### Boreman (1979)IV- 

### 6

Table LV-3. 

### Proportion 

### of 

### White 

### Perch Eggs 

### Spawned Eack

### Week During 

### 1974 

### and 1975 

### as 

### Used 

### in the ETM 

### Analyses‘
I f



### Year Week 

### Proportion

1974 516

# - 5/12 

### 0.0001 5/13

# - 5/19 0.0433 5/20

# - 5/26 

### 0.I626 5/27

# - 612 0.0950

613

# - 619 0.3633 6/10

# - 6/16 0.3153 

### 6/17

# - 6/23 0.0106 

### 6/24

# - 6/30 

### 0.0091



### 711

# - 715 

### 0.0007 ,­ 1975 5/4

# - 5/10 0.0012 /“ 5/11

# - 5/17 0.0382 5/18

# - 5/24 0.1567 5/25

# - 5/31 0.6149



### 611

# - 617 0.0917 6/8

# - 6/14 0.0160 6/15

# - 6/21 0.0737 

### 6/22

# - 6/28 0.0073 6/29

# - 715 

### 0.0003

afrom Table 9 inBoreman 

### (1979)

### IV-7

Table IV-4. Proportion 

### of 

### Alosa spp. 

### (Blueback

### Herring and Alewife) Eggs 

### Spawned EachWeek During 

### 1974 and 

### 1975 as

### Used In 

### the ETM

### Analysesa

Year



### 1974

1975 Week

4/29

# - 5/5

516

# - 5/12 5/13

# - 5/19 5/20

# - 5/26 5/27

# - 612

6/3

# - 619 

### 6/10

# - 6/16 4/21

# - 4/27 4/28

# - 513

514

# - 5/10 

### 5/11

# - 5/17 

### 5/18

# - 5/24 5/25

# - 5/31

6/1

# - 6/7 618

# - 6/14 6/15

# - 6/21 Proportion



### 0.0052 0.0134 

### 0.0598 0.8370 0.0243 

### 0.0583 0.0020 0 

### 0012 0 

### 0.1237 0.1631 0.4300 

### 0.2779 0.0023 

### 0.0001 0 

### 0017

afrorn

### Table 14 

### inBoreman 

### (1979)

### IV-8

Table 

### IV-5. Proportion 

### of American 

### Shad Eggs 

### Spawned Each 

### Week / / 

### During 

### 1974 a8 

### Used in the 

### ETM Analysesa

,--



### Year Week Proportion



### 1974 4/22

# - 4/28 0.0577 4/29

# - 5/5 0.1692



### 5/6

# - 5/12 0.0826 5/13

# - 5/19 0.0722



# - 5/20

# - 5/26 0.1687 

### 5/27

# - 6/2 0.3862

613

# - 619 0.0552 6/10

# - 6/16 0.0080
­ 

### 6/17

# - 6/23 0.0002 ?'



### a

### from Table 

### 21 in Boreman 

### (1979)

_ -

### IV-9



### Table 

### IV-6. Proportion 

### of 

### Bay Anchovy 

### Eggs 

### Spawned Each 

### Week During 1974 and 

### 1975as Used in

### the 

### ETM Analysesa

Year



### 1974



### 1975 

### Week

613

# - 6/9 

### 6/10

# - 6/16 6/17

# - 6/23 6/24

# - 6/30



## 711

# - 717 718

# - 7/14 7/15

# - 7/21 7/22

# - 7/28 7/29

# - 814

815

# - 8/11 8/12

# - 8/18



### 611

# - 617 6/8

# - 6/14 6/15

# - 6/21 6/22

# - 6/28 6/29

# - 715

716

# - 7/12 7/13

# - 7/19 7/20

# - 7/26 7/27

# - 812 

### Proportion

0.0078 0.5254 0.1486 0.0047 0.0035 

### 0.0569 0.1279 0.0627 0.0326 0.0118 0.0181 0.2784 0.0463 0.0096 

### 0 

### 0.3012 0.2838 0.0689 0.0098 

### 0.0020

a

### from Tables 27 

### and 28 

### In Boreman 

### (1979)

### IV-10



### estimated weekly standing crop 

### of Atlantic tomcod yolksac larvae occurred during the 

### firstweek 

### of sampling in1975 

### (Table 26, 

### Boreman 19791, which supports the validity 

### of this assumption. 

### The entrainment periods of the 

### six 

### fishpopulations selected 

### for 

### ETM analyses 

### are 

### listed in Table 

### IV-7.

4. 

### REFERENCES CITED


### Boreman, J. 

### 1979. Life histories 

### of seven fishpopulations that inhabit the Hudson River estuary. 

### 94 pp. 

### Exhibit EPA-198.

### IV-11

Table 

### IV-7. Entrainment Period Durations 

### for Selected Fish Populations Inhabiting the Hudson River During 1974 and 1975 as Used in the 

### ETM Analyses

Entrainment Spawning Entrainment Population Year interval (days) period (days) period (dates)a
Striped 

### bass 1974 1975 

### 65.5 63.5 63 

### 49 4129

# - 8/29 

### 5/11

# - 8/25 White 

### perch 

### 1974 1975 

### 63 69.5 

### 63 63 516

# - 9/2 

### 5/4

# - 917 1974 1975 

### b Alosa spp. 

### 63 63 49 

### 63 4/29

# - 8/12 4/21

# - 8/18 American shad 1974 60 

### 63 4/22

# - 8/16 Atlantic tomcod 1975 

### 91 C 

# - 319

# - 6/8 Bay anchovy 1974 

### 1975 74 74 

### 37 

### 63 6/3

# - 10125 

### 6/1

# - 1019
abeginning of 

### entrainment periods are based on dates when eggs 

### (or

tomcod 

### yolksac larvae) were 

### first sampled in the 

### TI Long River surveys, from Boreman (1979)
bblueback herring and alewife
C

### single cohort used due 

### to lack of

### data on the temporal distribution



### of Atlantic tomcod egg depositionCHAPTER V

W-FACTORS FOR 

### HUDSON 

### RIVER ICHTHYOPLANKTON ENTRAINED

### AT

BOWLINE, LOVETT, INDIAN POINT, ROSETON, AND DANSKAMMER

TESTIMONY OF

LAWRENCE

### W. BARNTHOUSE, PheD.


### K. DEVA 

### KUMAB, Ph.D. AND BERNADETTE La 

### KIRK

ENVIRONMENTAL SCIENCES DIVISION

OAK RIDCE NATIONAL LABORATORY OAK RIDGE, TENNESSEE

PREPARED FOR THE UNITED STATES ENVIRONMENTAL PROTECTION AGENCY REGION 

### I1

APRIL, 1979SUMMARY

This chapter contains estimates of W-factors developed from ichthyoplankton data collected at the Bowline, Lovett, Indian Point, Roseton, and Danskamner generating stations. Two sets of W-factors


### were developed 

### for each 

### of five populations: stripedbass, white perch, Atlantic tomcod, 

### bay anchovy, and 

# - (alewife and blueback herring). Alosa The chapter consists 

### of five parts:



### 1.

2.

3.



### 4.

5. An introductorydiscussion

### of severalmethodological problems associatedwith all estimates of W-factors 

### and of our approach to dealing

### with these problems: the calculation of two 

### sets of parameters, using two independent methods 

### of computation. 

### A description 

### of the two methods, the Modified Utility 

### (MU) and Gear Bias Cancelling (GBC) method, used to compute W-factors. Separate sets of W-factors for striped bass, white perch, Atlantic tomcod, bay anchovy, and Alosa computed using the MU and GBC methods. 

### A point by point discussionof our disagreements .with the data and methods used by LMS inExhibit 

### UT-3 to compute w-ratios. A comparison between W-factors 

### for each population calculated using the MU and GBC methods.

### 1 

### INTRODUCTION

Entrainment models employed by consultants for both the utilities and EPA to estimate conditional entrainment mortality 

### rates include a parameter, known as the w-ratio (utilities) orW-factor (EPA). This parameter 

### is intended 

### as 

### a measure 

### of the abundance 

### of organisms in power plant intake 

### water relative to their average abundance in an idealized cross-section 

### of the river in front 

### of a paver plant. The W-factor 

### is included inentrainment models primarily to account for the effects 

### of non-uniform distribution

### of organisms in the river on the number entrained. If, for example, larvae 

### are concentrated in thecenter 

### of the river while power plants draw cooling water from near shore, then the density of larvae inthe intake

### water 

### is likely to be 

### less than the average density of larvae inthe cross-section of the river in front

### of the intake. The same result can occur if organisms 

### are concentrated near thebottom of the river while cooling water is withdrawn primarily from the surface. In either case, the number entrained would be 

### less than would be predicted by a model that assumes that the density of organisms in pawer plant cooling 

### water 

### is equal to the cross-sectional average density 

### of organism intheriver. There are also distributions that can result inthe abundance of organisms inthe intakewater being greater than their average cross-sectional abundance inthe river: 

### if, for example, they congregate inshallow water near the shore (allHudson River power plants have shoreline intakes). In the past it has been argued that, for entrainable life-stages of stripedbass, the distribution of organisms in the 

### river is such

.that ingeneral they are less abundant inthe river strata from which

cooling water is drawn than in the entireriver cross-section, and thereforeW-factoss should on the average be 

### less than 1.0. Barnthouse et al. (1977) have sumnarized the positions 

### of both the 

### NRC staff and utilityconsultants at the time the Final Environmental Statement for Indian Point 

### Unit 3 was prepared. Results derived from 

### ORNL's striped



## v- 1

## -- v-2

bass young-of-the-year model (developed for 

### NRC) indicated that striped bass ichthyoplankton are concentrated 

### near the 

### river bottom or the shores, where they 

### are less susceptible 

### to downstream transport (Barnthouse 

### et al. 1977, p. 5-58). Moreover, a comparison of plant intake and river transect data collected at Bowline, Lovett, Roseton, and Danskanmer in

### 1973 showed that the aeasured densities of striped - bass life-stages 

### were generally lower 

### in samples collected at power plant intakes than insamples collected at the river transect stations (Barnthouse et 

### al. 

### 1977, p. 5-53). The 

### NRC staff concluded that W-factors (then called fI) for striped bass lifestages 

### were generally less than 1.0 but greater than 0.5 (Barnthouse et al. 

### 1977, p. 

### 5-60). Similarly, 

### inExhibit 

### UT-3 both 

### TI and 

### LMS employ W-factors that are generally 

### less than 

### 1.0. TI assumed (Exhibit

### UT-3, Sections 2-VI and 

### 2-VII) that W-factors 

### for all life-stages 

### of all species for which

- entrainment impacts estimated (stripedbass, white perch, Atlantic were tomcod, and American shad) 

### are equal to 

### 0.5. LMS concluded (Exhibit 

### UT-3, p. 

### 3-LV-541, based on plant and year specific intake and 

### river transect data, that both upper layer and lower layer w-ratios 

### are generally less than 1.0. Most 

### of the W-factors developed inthis testimony are smaller than 1.0. However, a substantial percentage ate larger than 

### 1.0, and the majority are larger than the value of 0.5 assumed 

### by TI. Many 

### of the w-ratios calculated by 

### LMS (Exhibit UT-3, Table 

### 3-IV-27) arebased on data that, 

### inour opinion, should 

### not be used for that purpose. More important, we believe that, because of biases inherent inthe methods used to calculatew-ratios, and because of the extraordinarily low precision of all estimates of ichthyoplankton abundance, neither 

### LMS' estimates nor any other single set of W-factors
can by itself be assumed tobe accurate. In Section 1.1 we discuss two seemingly insurmountable methodological problems associated with the estimation of W-factors. In Section 

### 1.2 we propose our solution to these problems: the use of two, rather than one, independently derived methods of estimating W-factors. The first, referred to as the Modified Utility 

### (MU) method, ~



### -

-

### v-3

is only slightly different from the method used by LMS 

### in Section 

### 3-IV of Exhibit UT-3. The second, referred to 

### as the Gear Bias Cancelling 

### (GBC) method, was developed in

### an attempt to eliminate biases introduced into 

### estimates of W-factors 

### by differences 

### in the types and methods 

### of deployment of the gear used 

### in collecting plant and 

### river ichthyoplankton samples. Sections 2 and 

### 3 contain, respectively, descriptions of the Ku and GBC methods and the results obtained when the two methods 

### are applied to data collected at Bowline, 

### Lovett, Indian Point, Roseton, and Danskamner (the specific data 

### sets used 

### are described inAppendix B of this exhibit). In Section 

### 4 we discuss our objections to the 

### LMS w-ratios. to the data used by INS rather than to the analytical treatment of the For the most part these are objections

data. A general discussion 

### of the W-factors 

### is presented inSection 

### 5.



### 1.1 

### METHODOLOGICAL PROBLEMS ENCOUNTERED

### IN ICHTHYOPLANKTON SAMPLING AND THEIR 

### CONSEQUENCES WSTH 

### RESPECT TO 

### THE ESTIMATION OF W-FACTORS 

### In sunnnarizing and criticizing the data and methods used by both the utilities

### and the 

### NRC staff to 

### estimate f (now called the 

### I W-factor) for striped bass, Barnthouse et al. (1977; Section 5.4.3) pointed oct two basic problems that severely limit the accuracy and precision 

### of those estimates.> The first of these 

### is insufficient quantity of data, caused by insufficient plant and river sampling effort during periods of high abundance 

### of striped bass ichthyoplankton, and by the inability of the gear to effectively sample older larvae and juveniles. The second is that themethods used 

### to collect ichthyoplankton 

### from the river invariably have been substantially different from those used to collect ichthyoplankton 

### at power plant intakes. Although two additional 

### years of data are now available 

### for most plants, these two sources of error still plague all attempts to obtain accurate empirical estimates of W-factors for striped bass and other Hudson River fishpopulations. In this section we briefly discuss the problems caused by small sample sizeand lack of gear comparability and argue that no sampling program can completely eliminate them.

### v-4

The collection efficiency 

### of ichthyoplankton sampling gear

# ,defined


### as the fractionof organisms in the path of the gear that 

### are captured and counted, 

### is strongly influenced 

### by the design of the gear and the

~ manner inwhich 

### it 

### is deployed. Therefore, ichthyoplankton densities computed from data collectedwith different gears 

### can differ substantially, even though the actual densitiesof organisms inthe

- paths of the gears 

### are identical. Since the methods used to collect ichthyoplankton atpower plant intakes on the Hudson River 

### are invariably different 

### from those used 

### at the river transect stations, W-factors computed as simple ratios of plant and river concentrations


### are subject to potentially severe biases. Carpenter (1979) has identified threemajor sources of such bias: gear avoidance, clogging of nets with detritus, and extrusion. According to Carpenter, the abilityof larval and juvenile fish to detect the presence of towed plankton nets (the gear used to collect the dataused to compute W-factors) and to subsequently evade capture 

### is a function of the sizeof the net, the speed at which it is towed,

~ and the presence or absence 

### of turbulence-creating structures (such as bridles) infront of the net. All other factorsbeing equal, the probability that an organism inthe path of a net 

### willbe able to swim out 

### of that path before 

### it 

### is capture.d decreases 

### as the diameter of the net opening increases. Similarly, the probabilityof escape decreases with increasingnet speed because, once the net has been detected, less time 

### is available for the organism to react and 

### swim out 

### of the path 

### of the net. The presence 

### of bridles, towlines, or other structures in front

### of the net mouth 

### is thought to increase the probability

### of escape by enabling organisms todetect the gear at a greater distance. Clogging 

### of a net with plankton or detritus reduces its filtration efficiency, 

### i.e., the fraction of the 

### water (as distinct from the fraction

### of organisms) in the path 

### of the net that enters the net mouth and 

### is filtered. According to Smith et al. (19681, clogging increases the incidence of gear avoidance by creating turbulence in front of the net. In addition to increased avoidance, clogging can lead to underestimates of the true density of organisms inthe water sampled by ,­

~ I

## v-5
causing overestimation 

### of the volume 

### of 

### water filtered. At Bowline, Lovett, and Indian Point, intake sample volumes have been estimated indirectly from measurements 

### of intake velocity rather than directly measured 

### by placing flawmeters inthe nets. 

### It is possible for eggs and small larvae to burst in

### a net 

### or to 

### be pushed through the mesh pores and lost. referred toby Carpenter (1979) 

### as extrusion, increases 

### as the tow speed The magnitude 

### of this bias,



### of the net 

### increases because the rate 

### of filtrationper unit net 

### area (and therefore the pressurewith which organisms 

### are pushed against the net

### 1 increases. In geceral, the 

### nets used at Hudson River power plant intakes have smaller mouth openings than 

### do the nets used to collect river transect samples (Carpenter 1979). Moreover, the velocity of sampling 

### is invariablymuch higher inthe river than 

### at power plant intakes. At theplants, 

### nets are simply lowered intothe intake flow. Intake velocities at Bowline, Indian Point, and Roseton 

### are on the average. 

### less than 

### 30 cm/sec (Carpenter 1979). Incontrast, the river transect samples 

### are collected by towing nets at a velocity of 60-150 cm/sec (Carpenter 

### 1979, Table 

### 3). 

### If the effects

### of net 

### sizeand sampling velocitywere the only biases present, then ichthyoplankton densities measured at power plant intakeswould be less than those measured 

### at the river transect stations, even inthe absence 

### of real differences in ichthyoplankton abundance. However, because 

### of the higher sampling velocity inthe river, it 

### is possible for life stages susceptibleto extrusion tobe sampled 

### less efficiently inthe 

### river than 

### at power plant intake stations. 

### In addition, the nets used to collect river samples have bridles and towlines while the nets used to collect plant samples do not (Carpenter 

### 1979). Increased gear avoidance due to the presence 

### of bridles probably offsets to some degree the biases caused by the greater size and sampling velocity of the gear used to collect the river samples. Carpenter (1979) concluded that the overall effect of the various biases is an underestimation 

### of ichthyoplankton densitiesmeasured at the Roseton, Bowline, and 

### Indian Point intakes relative to densities

### V-6

measured 

### at the corresponding river transect stations. However, the possibility exists that for some life-stages of some species at some plants 

### it may be the river concentrations that areunderestimated. At present it is not possible to quantify the relativebiases, and therefore it 

### is not possible to compute unbiased estimates of W-factors 

### using simple ratios of plant and river concentrations. Even if there were no gear biases, precise estimates of W-factors based on plant and river ichthyoplankton data would stillbe impossible to obtain. The reason for this unfortunate fact 

### is the extremely "patchy" nature of ichthyoplankton distributions. By patchy 

### we mean that the variation among the numbers of organisms collected 

### in different samples 

### is greater than would be expected if organisms were randomly distributed throughout the river. According to Cassie (19631, the proper technical term for this type of distribution 

### is "overdispersion." Based 

### on his review of the literature, Cassie concluded that planktonic organisms 

### are nearly 

### always overdispersed. In the Final Environmental Statement for Indian Point Unit 

### 3 

### (U. 

### S. Nuclear Regulatory Commission 

### 19751, the 

### NRC staff examined ichthyoplankton densities observed inindividual samples collected at Bowline, LOvett, Roseton, and Danskammer in 

### 1973. The staff concluded that the distribution of these densities

### was "strongly skewed toward zero." In other words, many samples contained no organisms while 

### a few contained many organisms. This is a characteristic of overdispersed distributions 

### The major consequence of overdispersion, as far 

### as the computation of W-factors 

### is concerned, 

### is that estimates 

### of the mean concentration 

### of ichthyoplankton present at a power plant intake or inthe river 

### will have high standard errors, even if large numbers of samples are collected. We will illustrate this problem by examining 

### several 

### sets of abundance data 

### for stripedbass post yolk-sac larvae. These data
were collected in 1975 

### at the Roseton intake, the Roseton/Danskaarmer river transect stations, and the Indian Point intake and discharge. r-­

.,

,-

## v-7

The sampling program atRoseton 

### is typical of those conducted 

### at most Hudson River power plants. In Table 

### V-1 we have tabulated mean densities, numbers of samples, standard errors, and 95%confidence intervals for striped bass post yolk-sac larvae collected at the Roseton intake and at the Roseton/Danskamer river stations on sampling dates between May 29 and June 19, 

### 1975- Results 

### are presented separately 

### for day and night. The most precise results were obtained at night at the Roseton intake

### on June 2. As measured by the ratio

### of theupper and 

### lower 95%confidence bounds, the actual density of stripedbass post yolk-sac larvae (neglectinggear bias) 

### is known 

### mly towithin a factor 

### of two. On 

### all other nights the precision

### of estimates 

### of the mean density

### of post yolk-sac larvae 

### at the Roseton intake 

### is much lower. 

### Even though a mean density 

### of 100 larvaell000 m was observed on the 

### 3 night 

### of June 5, the precisionof this estimate is so low that it is 

### not significantly different from zero. The daytime 

### estimates at the Roseton intake are even 

### less precise than the nighttime 

### estimates. Only two of the five means 

### are significantlydifferent from zero, and themost precise (June 2) isknown only towithin a factor of 

### 4.6. The precision 

### of the 

### estimates 

### of the abundance of post yolk-sac larvae at theRoseton/Danskamaner river stations is lower still. None 

### of the daytimemeans is significantlydifferent from zero. The upper and lower 

### 95%confidence bounds around the two nighttime means that 

### are significantlydifferent from zero differby greater than a factor of 6. The sampling program conducted at the Indian Point intakes and discharge canal 

### is the most intensive conducted atany Hudson River power plant. Therefore, the precision of the abundance 

### estimates obtained 

### at Indian Point should be higher than that obtainable 

### at any other plant. However, the intensive sampling effort 

### at Indian Point appears tohave prodwed estimates of the abundance of striped bass post yolk-sac larvae that are only marginally more precise than those obtained atRosecon. During the period from May 27-June 24, 1975, which spans the period of peak abundance of stripedbass post yolk-sac larvae inthe Indian Point vicinity, between 

### 36 and 69 samples were

# -- -- v-a

Table V-1. stripedbass post.yolk-sac larvae attheRoseton Theprecision of estimates of the densityof intake andriver transect stations, 1975a

DensitycMwim
ROSETON INTAKE (DAY) 12.8 622.0 1762.3 582.5

16.6 14.8 ROSETONINTAKE (NIGHT) 7.7 612.2 157.1 467.7

30.3 ROSETON INTAKER DAY)^ 66.9 187.2 565.9 364.2

12.7 Ratio of 95%confidence upper and bounds lowerbounds

0-5.4 53.3-246.1 138.4-867.0 0-489.7 0-12.5 0-9.0

0-10.4 320.0-626 -8 2.0 0-316.5 201-3-610.7 3.0

13.9-44.3 3.2

0-43-5 0-126.1 0-340.5 0-262.5 0-7.4

0-340.2 143.9-959.9 123-6-808.1 0-1088.2 0-8.4

rStandard error ofmean

1.6 45.9 165.6 99.2

3.3 2.5

1.7 63.9 50.3 47.6

1.2

9.4 27.4 77.2 57.8

1

### .a Date Nb Mean Minimum

May 29 8 1.6 0 June 2 18 149.7 0 June 5 12 502.7 0 June 9 6 231.8 0 June 12 5 3.3 0 June 19 6 2.5 0
May 29 5 2.8 0 June 2 8 473.4 66.7 June 5 3 100.2 0 June 9 3 406.0 312.5 June 19 2 29.1 27.9
May 29 7 20.0 3.5 June 2 7 57.6 0 June 5 7 147.5 0 June 9 7 118.0 0 June 19 7 2.9 0
May 29 7 113.2 0 June 2 7 551.9 100.6 June 5 7 465.8 153.7 June 9 7 466.4 49.0 June 19 7 3.6 0 ROSETON INTAKE (N1GHT)d 652.7 90.8 1340.5 163.2 1207.5 136.9 1907.2 248.7

12.6 1.9
'Source of data isdescribed In Appendix B. bTotal number ofsamples collected (depths and stations combined). 'Number of organismsper 100m 3


# . dAll transectstations cmbined.



### _-

### v-9

collected every night from the Indian Point Unit 2 intake and between 

### 18 and 71 were collected from the discharge canal (Table 

### V-2). Even with this level of effort, the standard errors associated withestimates 

### of mean abundance 

### on each date 

### are large. On the dates on which the most precise estimates were obtaiced (June 17 

### at the intake and June 

### 3 at the discharge canal), the mean abundance of striped bass post yolk-sac larvae 

### is known only towithin a factor of 2. 

### On the date on which the highest abundance 

### was observed (June 101, the mean densities (neglecting gear 

### bias) are known only towithin a factor of 3.5 at the intake and 

### 4.5 at the discharge. Although 

### we have not performed the above analysis for all life-stages of 

### all species collected at all plants during 

### all years for which data are available, there 

### is no 

### 5 priori reason to expect other ichthyoplankton abundance estimates tobe
more precise than are those that 

### we have examined. Allestimates of W-factors obtained 

### from thisdata, both ours and those 

### of the utilities, 

### are low in precision. Even large numerical deviations from 1.0 may be attributable to sampling error, as can between-year differences inthe calculated W-factors for the same population and life stage. 

### No single analytical method can completely
overcome the inherent limitations on accuracy and precision caused by non-comparable samplingmethods and the patchy nature of ichthyoplankton distributions.

1.2 WKY TWO 

### ESTIMATORSARE BETTER THAN 

### ONE Ina well-known and frequently cited paper, Levins 

### (1966) discussed the dileuma faced by a biologist attempting tomodel the evolution of a hypothetical plant or animal population (e.g., the Hudson River striped bass stock). Such populations, composed 

### of individual organisms 

### of different ages and genetic constitutions, interact with a heterogeneous, fluctuating environment and with other populations (e.g., their competitors, predators, and prey) that are themselves evolving. Levins concluded that biological systems 

### are so complex and poorly understood that it is not practical to attempt to build a singlemodel that faithfully reflects all of the bewildering complexity that characterizes

## v-io

E I6 c 0 E U Q, c 4a 0 I v1 d eel 

## % > L
c V I VI 1 Y F 

# e, Y VI 0 n VI VI ro D U 

### al n .r in L Y VI

00000 ~00000



## c;

1 > Q) c n nIe m

E 

# 8 d L Q) n VI 

# . v) 

# 5 N .r c U d rn 

### !i L 0 cu r - N cc 0 m c 0 

# f c a Y

r ­ 4 

## 5 c, z v) V U

### v-11

the interactionof an evolvingpopulation with 

### its environment. 

### As a solution to the dilemma, he proposed the 

### use 

### of clusters of models: alternativemodels 

### of the same phenomenon, each constructed using 

### a different 

### set 

### of simplifying assumptions. If only one suchmodel 

### were used, 

### it would not be possible to determine whether results derived from the model 

### were realisticor whether they 

### were artifacts introduced by the simplifying assumptions. Suppose, however, that two 

### or more independent models, each simplified in

### a differentway, lead to 

### similar results. In this case 

### it is possible tohave 

### some confidence that the realistic aspects of the models, rather 

### than the simplifications, 

### are responsible for those results. Truth, Levina suggested, can be found 

### at the "intersection of independent 

### lies

# ." Although proposed for a different problem than the one considered here, Levins' solution 

### is as valid 

### an approach 

### for estimatingW-factors
ae it 

### is for studying the evolution of populations. 

### The method used by 

### USto compute w-ratio8 

### is simplified, inthat the sampling gear used to collect ichthyoplankton from the 

### river and from power plant intakes and discharges are assumed tobe equally efficient at collecting organisms, regardless 

### of differences 

### in size, structure, and

### means of deployment. Since the gears undoubtedly differ 

### inefficiency

### inways that have not been (and perhaps cannot be) 

### accuratelymeasured, the 

### w-ratios derived using 

### US' method 

### are influenced by biases that cannot be quantified. Such biases could cause 

### results obtained 

### from the 

### Beal 

### TheLife Cycle model to be seriously inerror. In order to ensure that resultsobtained from the Empirical Transport Model (and especially resource management decisions based 

### on thoee results) are not overly influenced by 

### errors inthe estimation of W-factors, 

### we developed not one, but two 

### sets 

### of W-factors. The two 

### sets were obtained using independent methods, each 

### of which employs different simplifying assumptions. 

### Our firstmethod, the Modified Utility 

## o!lU) method, 

### is conceptually identical tothe approach taken by 

### LMS inthat the collectionefficiencies of all sampling gears are assumed to

### be equal. There 

### are no major differences between the tvo

. ..

## v-12

other than the fact that one 

### is used to compute w-ratios for 

### a 2-layered model of the 

### river and the other to compute depth-averaged W-factors for 

### a single-layered model. Our second method, the 

### Gear Bias Cancelling (GBC) method, 

### is entirely different

### in concept. The efficienciesof the gears used to collect ichthyoplankton from the 

### river and from power plants 

### are assumed tobe different, and‘thecomputational procedure 

### is designed specifically to cancel out biases caused by these differences. Simplifying assumptions about the distributionof organisms inthe



### . river and about the flow of water into 

### a power plant intake

### are employed 

### as part of the GBC method, and these assumptions may introduce biases into the results. However, such biases 

### are likely todiffer in magnitude and/or direction from the biases associated with W-factors calculated using the MU method. Similarly, W-factors computed using these two methods 

### are influenced 

### indifferent ways by sampling errors (the sensitivities

### of the

### MU and GBC W-factors tobiases and sampling
errors 

### are discussed in Section 

### 5). If conditional entrainment mortality 

### rates obtained from the 

### Empirical Transport Model 

### are similar, regardless of which 

### set of W-factors 

### is used, then we have
some confidence that the 

### ETM results 

### are not seriously compromised by
errors inour 

### estimates 

### of this parameter.

### V-13

2. 

### METHODS USED 

### TO 

### COMPUTE W-FACTORS FOR 

### HUDSON 

### RIVER POWER FLANTS

In this Section we describe the two methods used to compute W-factors 

### for Hudson River ichthyoplankton entrained at the Bowline, Lovett, Indian Point, Roseton, and Danskammer power plants. Our two methods 

### are 

### similar to the utilities' method 

### inseveral

### respects. First, separateW-factors 

### are computed for each life-stage (eggs, yolk-sac larvae, post yolk-sac larvae, and, 

### for some species, juveniles) 

### of each population. Second, U-factors 

### are computed separately 

### for day and night, with day and night having the definitions set forth by the utilities 

### on p. 

### 3-IV-32 of Exhibit

### UT-3. Third, 

### in general only paired 

### sets of plant and river data collected 

### on the 

### same dates 

### were used. Unlike the utilities' w-ratios, which 

### are computed separately 

### for upper and lower 

### layers 

### of the river and the power plant intake bays, our W-factors 

### are averaged over all depths. Moreover, unlike the utilities,

### we included data collected on alldates on which organisms 

### were caught 

### in either the plant 

### or the river, regardless of how law the densities might have been (this procedure expands the data base slightly and should, intheory, slightly increase the precision 

### of the resulting W-factor estimates).

2.1 MODIFIED

### UTILITY (MU) METHOD Three steps are involved 

### inthe computation 

### of a W-factor for a given species and life-stage using the MU method:
(1) calculation of average plant and river densities for each sampling date on which the life-stage was collected, (2) calculation 

### of mean seasonal plant and river densities, 

### i.e., the average densities over all the sampling dates on which the life-stage 

### was collected, and 

### (3) calculation 

### of the W-factor as a ratio of the seasonal plant and river densities.V- 

### 14

A number 

### of samples (sometimes a large number) are collected 

### at each location (plant or 

### river) during each 

### timeperiod (day 

### or night). 

### .- River samples 

### are collected at several stations; samples 

### are collected

.I 

### at three depths both inthe river and at the parer plants. Several 

### sets of plant samples may be collected during 

### a given day or night. 

### We treat 

### all plant or river samples collected 

### on the 

### same date during the
same 

### time period 

### as if they are replicate samples drawn from a common sample poplation. 

### All of these 

### samples 

### are pooled inorder to calculate 

### the average day or night plant or river density of 

### a particular fishpopulation and life-stage. 

### That 

### is, the average density 

### is equal to the 

### sum of the numbers 

### of organism collected in 

### all of the samples divided by the 

### sum of the sample volumes:



### m

r ".,

where

### "ijk number 

### of organisms caught inthe kth sample collected 

### at location 

### i (plant 

### or river) 

### on date

### j, Vijk

### = volume of kth sample collected at location 

### i 

### on date

### j,


### m = number 

### of samples collected 

### at location 

### i 

### on date

### j during

r_ either 

### daytime or nighttime,
n.. sum of the 

### n..

13 1Jk'8over 

### allvalues 

### of k,
V 

### = sum of the v.. 

### 's over all values 

### of k.

ij. lJk
The above computational procedure 

### is equivalent to weighting each sample according to 

### its volume, on the assumption that the greater the volume of a sample, the more reliable the resulting density estimate.

- 

### In order to demonstrate 

### this, we define a weighting factor,

w.. t vi jk

I ­ 

### ijk v.. 1J

..

### V-15

If d.. 

### ljk

### /v.. Ilk 

### (= 

### n.. ijk 

### is the density 

### of organisms 

### ina particular sample, then we can define a eighted average density as



### m 

### D!. = 

### C wijkdijk 

# . (v-2)

'Jk k5;1



### From the definitions 

### of Vijk and dijk,

m



### m 

### C nijk/v;. 

### k=l 13.

a

### jij./V.. ij.



### = 0.. 13

Given the plant and river densities 

### for each date, the seaaonal plant and river densities ar computed as unueighted averages of the respective 

### D..'s. Finally, theW-factor 

### is computed 

### as the ratio

### of 

### U the seasonal plant and 

### river densities. Since only paired 

### sets of

. plant 

### and river data 

### are used to calculateW-factors, the number 

### of sampling dates used 

### to compute the eeasonal plant density 

### is always equal to the number used to compute 

### the seaso'nal 

### river density. Therefores



### 07-31

where

### D1, sum 

### of plant densities over 

### alldates', 

### D2. = sum 

### of river densities over 

### all dates.

The seasonal W-factors are, equivalent toweighted averages 

### of W-factors computed separately for each date, with the weighting factor

### V-

### 16
assigned to each date being a function of the density of organisms in the river on that date. 

### In order to demonstrate this

### we can rewrite 

### Eq. (V-3) as:



### T W = 

### fC D2j(Dlj/D2j)l/D2. 

### j=1


### T (v-4)

T = 

## c w~(D~~/D~~) 

# , j=1

where 

### T = number 

### of sampling dates on which organisms were collected,
w

### j = D2j/D2. = weighting factor for date 

### j.

The quotient 

### (D1 J 

### ./D 

### 2J

.), the ratio of plant density to river density
on date 

### j, is simply an 

### estimate of the W-factor OG date 

### j. The weighting factor, 

### wj, 

### is the ratio 

### of the river density 

### on date 

### j to the sum 

### of river densities over alldates. If the life-stage in question 

### were very abundant on date 

### j relative to its abundance on other dates, the weighting factor would be large. If, on the other hand, the density in the river wererelatively law on date 

### j, the weighting factor would be small. 

### Thus, 

### our W-factor estimator gives the greatest weight to dates on which organisms were most abundant in

. the river. This 

### is a desirable property because these are the dates on which it 

### is most important to know the fraction of organisms inthe vicinity of a power plant that are entrained and killed. The only deviations from the computational procedure described above occurred in the treatment 

### of the intake and discharge data at Indian Point. The sampling conditions at the intake and discharge stations are quite different. Different gears are used, and sampling

. . velocities are several times higher at allof the discharge canal stations than at the Unit 1, 2, and 

### 3 intake stations. Therefore, the

### V-17

intake and discharge 

### data 

### were not treated as 

### if drawn from a common sample population. Instead, intake and discharge densities on each date 

### were computed separately using 

### Eq. (V-1). Like the utilities (Exhibit UT-3, p. 3-IV-371, we then computed plant densities for each date as unweighted averages of the intake and discharge densities. The W-factors 

### were then computed from 

### fEq. (V-3)1.

2.2 GEAR BIAS CANCELLING (GBC) 

### METHOD Because of differences 

### inthe types and methods of deployment of sampling gear between river and plant, direct comparisons between the calculated plant and river abundances probably do not yield unbiased estimates of W-factors. Although the differences in sampling techniques should generally result inrelative underestimates of plant abundance (Carpenter 19791, it 

### is conceivable that in some cases (e.g., because of greater extrusion inriver samples) it is the river abundance that 

### is underestimated. Because themagnitude and directionof biases due to differences insampling technique cannot be quantified, we have attempted to eliminate the problem of gear bias by developing 

### a new method of estimatingW-factors. This method, called the Gear Bias Cancelling (GBC) method, 

### is based on a two-layered model 

### of the river 

### cross-section that 

### is essentially identical to that used by LMS as the basis for the w-ratios presented in Section 3-IV of Exhibit UT-3. The upper and lower layers of the river are defined, respectively, as the 

### water masses lying above and below an imaginary boundary exactlymidway between the surface and bottom, from shore to shore. The upper and lower layers 

### of an intake bay are defined similarly. Like the calculation 

### of LMS w-ratios, the'calculation of W-factors using the GBC method involves comparing densities 

### of organisms observed in the upper 

### (or lower) layer 

### of an intake bay to corresponding densities observed in the upper (or lower) layer of the river. Thus,
as an intermediate stepinour procedure, we calculate w-ratios that


### are identical in concept to those 

### of the utilities. However, rather than applying OUT w-ratios directly to a two-layered version of the Empirical Transport Model, we use them to compute depth-averaged

### V-18

W-factors inwhich the biases caused byunequal plant and river gear

._ efficiencies 

### are cancelled 

### out. In order to demonstrate the bias cancellingprinciple that 

### is thebasis for.the GBC method, 

### we define:


### Dw,Dm,DpL,DEU. = actual densities of organisms inthe upper and lower 

### layers of plant and 

### river,
e 

### = efficiency 

### of plant sampling gear, 

### f 

### = efficiency 

### of river sampling gear, eDmrfDIUl,eDpL,fDm

### = observed densities of organiems inthe upper. and lower layers 

### of plant and river.

From the definition of 

### a w-ratio (Exhibit UT-3, p.' 

### 3-IV-30),



### w

U 

### = 

### eD

### Pu/fDw and 

### wL 

### = e D

### PL /fDm



### The 

### w-ratios, as calculated directly from the observed plant and river densities, 

### are biased 

### estimators of the ratios 

### of actual plant and river upper and lower layer densities. However, if the ratioof v-ratios 

### is calculated, the gear bias.terms e and f cancel out:

(

### v-5

### 1

In the GBC method, ratios 

### of upper and lover layer w-ratios areused 

### to compute W-factors that 

### are independent 

### of biases caused by unequal plant and river sampling efficiencies. The 

### GBC method employs simplifying assumptions about the distribution

### of organisms inthe upper layer of the river and about the flow 

### of 

### water into a power plant intake. 

### For this reason

### we use it as
a complement to, rather than a replacement for, the Modified Utility 

### (MU) method. Moreover, since the Bowline plant draws water from Bowline Pond rather than directly from the river, it is unlikely that

### v-19

the upper and lower layers of the Bowline intake bay 

### are inany way related to the upper and 

### lower layers 

### of the river. For this reason the GBC method 

### is not used to

### estimate W-factors 

### for Bowline. 

### For this plant, the Modified Utilitymethod can be used to compute 

### estimates 

### of W-factors that 

### are free 

### from gear bias, using the Bowline Pond ichthyoplankton data instead of intakedata. The assumptions required by the GBC method 

### are discussed indetail inSection 2.2.1; the computational procedure 

### is explained in Section 2.2.2.

2.2.1 Discussion of Assumptions The 

### GBC method 

### is based 

### on four assumptions: (1) There are no lateral trends inthe abundance of organisms in theupper layer of the river. Such trends may, however, exist inthe 

### lower layer. There 

### is no active avoidance 

### of the intake structureby (2)


### (3)



### (4) entrainable ichthyoplankton. Power plants draw 57%

### of their cooling 

### water from the upper layer 

### of the river and 43% 

### from the 

### lower layer. 

### All water in the upper layer 

### of an intake bay 

### is drawn from the upper layer of the river. Water inthe lower layer of an intakebay cmes from both layers 

### of the river. The first assumption, that of absence of lateral trends in abundance inthe upper layer 

### of the river, is consistentwith data 

### on the lateraldistribution

### of ichthyoplankton reported inExhibits UT-4, 

### UT-6, and 

### UT-?. In Sections 7.4.1.5.1 and 7.4.2.6.1 of Exhibit 

### UT-4, TI reported results obtained from studiesof the lateral distribution of stripedbass yolk-sac and 

### post yolk-sac larvae inthe Cornwall vicinity. 

### The lateral distribution 

### of yolk-sac larvae at the surface and 

### at the bottom 

### was studied 

### on May 

### 22-23 and May 28-29, 1975. 

### On May 

### 22-23, no statisticallysignificant differences in densitybetveen east, channel, or west zones or between surface and bottom stratawere found (Exhibit 

### UT-4, Fig. 7.4-8). On May 28-29, the surface densityof yolk-sac larvaewas significantlylower inthe channel zone than inthev-20

west zone. The lateraldistribution of post yolk-sac larvae 

### was studied 

### on May 28-29 and on June 

### 13-14, 1975. Although statistically significant trends in lateral abundance were noted 

### in the bottom layer
on June 13-14, no such trends were found at the surface (Exhibit UT-4, Fig. 

### 7.4-18). Exhibit UT-6 contains information on the lateral distribution of white perch, stripedbass, Atlantic tomcod, Alosa, and bay anchovy eggs and larvae inthe Roseton/Danskamer vicinity from 1974 through 1976. 

### No consistent lateral trends 

### inabundance 

### were observed 

### at any depth for white perch or striped bass eggs or larvae (Exhibit UT-6, Tables 9.1-2, 9.1-4, 9.1-8, 9.1-10). There 

### is some evidence for 

### a trend in lateral distribution of Atlantic tomcod larvae in the Roseton/Danskamner vicinity (eggs were collected 

### on too few sampling

. dates for trends tobe observed). Data obtained 

### on five sampling dates


### are listed inTable 9.1-15 of Exhibit UT-6. Both at the surface and 

### at the bottom, Atlantic tomcod larvae were most abundant at the 

### west station and least abundant 

### at the east station on three 

### of the five dates. Stronger evidence for a trend in

### lateral abundance 

### exists for 

### Alosa eggs and larvae. 

### Eggs have been consistently most abundant at the west station 

### at all depths (Exhibit UT-6, Table 9.1-16). Larvae have been consistentlymost abundant at the 

### east station at alldepths (Exhibit UT-6, Table 9.1-18). A plausible hypothesis explaining the observed trend in larval distribution is presented on p. 9.1-55 

### of Exhibit UT-6: the higher abundance of larvae at the east stationmay be related to 

### its proximity to the mouth 

### of Wappinger's Creek, 

### a possible spawning 

### site for large numbers of alewife and blueback herring. Although the egg distribution data indicate greater Alosa spawning activity on the west side of the river than on the east side,


### no explanations 

### are proposed inExhibit UT-6. Data presented in Table 9.1-22 

### of Exhibit UT-6 indicate 

### no lateral trends in larval bay anchovy abundance 

### at any depth. Data presented inTables 9.1-4 and 9.1-8 of Exhibit UT-7 do 

### not

' 7 ' reveal any consistent lateral distribution patterns for larval white perch or striped bass in the Bowline vicinity. White perch larvae

### v-21

were, as noted on p. 

### 9.1-12 of Exhibit

### UT-?, more abundant 

### at the channel 

### station than at the 

### east or 

### west stations 

### on the dates 

### of peak abundance 

### in 

### 1975 and 

### 1976, but no such pattern was observed 

### in 

### 1973 or 

### 1974. The highest densities 

### of eggs 

### of both species appear 

### to be found at the 

### east and 

### west stations (Tables 

### 9.1-2 and 

### 9.1-71, with lowest densities at the channel stations. Unlike the pattern observed 

### at Roseton/Danskamner, there appears tobe no 

### east-west trend inabundance 

### of Atlantic tomcod (Table 

### 9.1-11) or 

# - Alosa (Table 9.1-14) larvae inthe Bowline vicinity. Few Atlantic tomcod 

### or Alosa eggs have been collected 

### at 

### Bowline, and thus there 

### is no 

### lateral distribution data for this life-stage 

### of either species. Although 

### no lateral trends in abundance 

### of bay anchovy eggs have 

### been observed inthe 

### Bowline vicinity (Exhibit UT-7, Table 

### 9.1-171, such 

### a trend does 

### exist for 

### larval bay anchovy (Exhibit 

### UT-7, Table 

### 9.1-181. Densities of larvae


### are usually lower 

### at the channel stationthan 

### at the shallower 

### east and west stations. In summary, our 

### first assumption 

### is generally supported by results presented inExhibitsUT-4, UT-6, and 

### UT-7. These results indicate that consiszent 

### lateral trends 

### inabundance of eggs and 

### larvae 

### of the Hudson Riyer fish species considered in this testimony 

### are the exception rather than the rule. There 

### is no direct evidence with which to 

### test our second assumption, that of the absence 

### of active intake avoidance by ichthyoplankton. Active intake avoidance 

### is, or course, impossible for 

### eggs and probably impossible for yolk-sac larvae. 

### It 

### is at 

### least theoretically possible 

### for post yolk-sac larvae and earlyjuveniles to detect alterations 

### incurrent patterns caused by the intake 

### flow and to 

### react to these alterations. Whether detection actualiy does occur, and whether any 

### of these organisms escape the intake flow once it 

### is detected, 

### are unknown. Our third assumption, that power plants draw 

### 57%

### of their cooling 

### water from the upper layer 

### of 

### the river and 

### 43% from 

### the lower layer, 

### is derived from the LaSalle hydraulic model study, described on p. 

### 3-IV-62 of Exhibit UT-3. Although &he LaSalle study was designed to'5-22

' predict patterns of water withdrawal for the Indian Point plant, LMS has argued (Exhibit 

### UT-3, p. 

### 3-IV-62) that the 

### results are also applicable at other Hudson River power plants. In the absence of hydraulic model studies for Lovett, Roseton, and Danskamner, 

### we, like LMS, have applied the Indian Point results to allother plants. Our fourth assumption, that 

### allof the 

### water inthe upper 

### layer 

### of
an intake bay 

### is drawn from the upper layer of the river, while water in the 

### lower layer of 

### an intake bay 

### is drawn from both layers 

### of the river, 

### is not testable with any existing data. It is employed because it 

### is the simplest assumption that 

### is consistent with the vertical velocity profiles observed 

### at power plant intakes. If 57%of power plant cooling

### water 

### is drawn from the upper layer of the river and 

### 43% is drawn from the lower layer, then 

### it must be true that either 57%

### of the total flow enters the plant through the upper half of the intake bay, or 

### else water from the upper layer of the river enters the lower half 

### of the intake bay. 

### If 57%of the total flow 

### is drawn through the upper half of the intake bay, then themean velocity of water in the

. upper half should be 

### 1.33 (0.57/0.43) 

### times 

### as high as the mean velocity inthe lower half. Table 9.1A-4 

### of Exhibit UT-7A shows that 

### at Bowline the ratio of velocities of 

### water entering 

### the upper and 

### lower halves 

### of the intake bay 

### is much too 

### small toresult in.57%of the total flow being drawn inthrough the upper half. The greatest difference in velocities 

### is observed at law tidewhen all 

### sixpumps are operating. In this case the ratio of surface to bottom velocities 

### is 1.17 (0.83/0.71). However, the average velocities in the upper and lower layers 'are probably better estimated, respectively, by the mean of the surface and middepth velocities and the mean of the middepth and bottom velocities. If these 

### latter mea~ures

### of velocity 

### are compared, then themean velocity in the upper layer of the intake bay 

### is only 1.08 (0.785/0.725) times as high as that inthe lower layer. Texas Instruments measured velocity profiles at Indian Point unit 2 travelling screens in 1974 (Exhibit 

### EPA-92B). In 10 out 

### of 16 experiments, no statistically significant differences between the
veLocities measured at different depths 

### were detected. Approach

# v-23

velocitiesmeasured on the upper halves of intake screens 

### were substantiallyhigher than those measured 

### on the 

### lower halves only 

### in four 

### cases 

### in which 

### screens were fittedwith experimental fish collection baskets (Exhibit 

### EPA-gZB, Table 

### 111-6). 

### No consistent vertical trends invelocity

### were observed inexperimentsconducted at unbasketed screens (Exhibit

### EPA-92B, Table 

### 111-7). Thus,. vertical velocity profiles observed 

### at Bowline and Indian Point indicate that when differences between velocities 

### inthe upper layer and 

### the lower 

### layerof an intake bay 

### exist, the velocity inthe upper layer 

### is usually higher than 

### inthe 

### lower layer. 

### Hawever, euch differences

### are not found consistently and 

### are not large enough to result 

### in57%

### of the intake flow being drawn 

### in through the upper half 

### of the intake bay. Therefore, we feel safe inassuming that 

### erne water from the upper layer 

### of the river 

### is present inthe 

### lover layer of an intake bay. 

### Given that some 

### water 

### from the upper layer 

### of the 

### river enters the lower half 

### of an intake bay, the simplest possible way toapportion.the 

### water coming from the upper 

### and lower layers 

### of the river 

### is toaesume that:

### (1) 50%

### of the total cooling 

### water flaw enters the upper half 

### of the intakebay and 50%enters the lover half. (2) The 

### 43%of the cooling

### water flow that 

### is drawn from the lower 

### layer 

### of the river allenters the lower half 

### of the intakebay. Given these two assumptions, the remaining

### water entering the plant through the lower half of the intake bay, representing 

### 7%

### of the total flow, 

### is dram from the upper 

### layer 

### of the river. Therefore 86% (43/50) of the water 

### in the lower 

### layer 

### of the intakebay ie drawn 

### frpm the lawer 

### layer of the river and 14%

### is drawn from the upper 

### layer. The remaining 50%

### of the total flow, alldram from the upper layer 

### of the river, enters the upper half of the intakebay.

### V-24

2.2.2 Description of Computational Procedure The first stepinthe procedure for calculating 

### a W-factor for 

### a particular fish population and life-stage 

### at 

### a particular plant 

### is the estimation of seasonal upper and 

### lower 

### layer w-ratios. Like LMS (Exhibit 

### UT-3, Section 3-IV), 

### we use the surface and middepth samples from plant 

### or river 

### to estimate the densityof organisms inthe upper layer and the middepth and bottom samples to

### estimate the density 

### in the 

### lower layer. The plant and river upper-and lawer-layer densities for each date are computed using the procedure described in Section 

### 2.1 for the Modified Utility 

### (MU) method: all of 

### the samples collected 

### on a given date from a given layer during a given 

### time period (day 

### or night) 

### are pooled 

### [Eq. 

### (V-111. In order to compute valid upper- and lower-layer w-ratios under the assumptions described in Section 2.2.1, the estimated densities of organisms 

### in the upper and 

### lower layers of an intake bay should be 

### estimates, respectively, of the densities of organisms incooling 

### water drawn from the upper and 

### lower layers of the 

### river. However, the lower layer of an intake bay contains

### water drawn from the upper layer 

### of the river. Since the upper and lower layers of the river may contain different densities 

### of organisms, the observed density of organisms in the lawer layer of an intakebay must be adjusted to account for the presence of water drawn from the upper layer of the river. 

### We define:



### dPU = observed density of organisms in

### water drawn from the upper layer 

### of the river, d;L 

### = observed density of organisms inthe 

### lower layer of the intakebay, reflecting the

### mixing of 

### water drawn from both 

### layers of the river, dPL 

### = adjusted density of organisms inwater drawn from the lower layer of the river.

Since 86%of the water inthe lower layer of the intake bay is drawn from the lower layer of the river and 14%

### is drawn from the upper layer (Section 2,2.1), we express the relationship between the adjusted ..

--.

, 'I'

-~

# -

-I-

\,

### V-25
density inthe lower layer of the intake bay and the observed upperand lower-layer densities using the following equation:


### (V-6)
Rearranging, the adjusted density 

### of organisms in

### water drawn from the lower layer of the river 

### is givenby:

Equation 

### (V-7) is used to adjust the density 

### estimates for the lower

' layer 

### of the intakebay as computed from 

### the raw data, 

### to account 

### for

the presence of 

### water drawn from the upper layer 

### of the 

### river. Occasionally, the observed density inthe 

### lower layer 

### of the intake bay 

### is less than 14%

### of the observed density inthe upper layer. 

### In these
cases the adjusted density inthe 

### lower layer 

### is set 

### equal 

### to 0. After the computation and adjustment of density estimates for each date, seasonal plant and river upper and 

### lower layer densities 

### are calculated 

### as unweighted averages over all dates (i.e., the 

### same procedure used inthe MU method). Next, w-ratios are obtained by computing the ratio 

### of the seasonal plant upper layer density to the seasonal river upper layer density and 

### of the plant 

### lower layer density to 

### the river lower 

### layer density:



### We next use the gear bias cancelling principle to compute an estimate of

### w

### Lwhich if free from gear bias. First, we form a ratioof w-ratios that can be used to express the observed value of w

### L as a function 

### of the observed value of 

### w

### U:
(wu 

# - w,)/(w, + w,) = 

### A 

# . (v-10)

### V-26

This particular ratio, rather than other possible ratios (e.g., 

### w 

### /w ), 

### UL 

### is employed because the distribution

### of possible values of 

### A is symmetrical around 0 and boilnded by 

### 1.0 and 

### -1.0. If the distribution
were asymnetrical, then sampling error could cause estimates of 

### A tobe severely biased. It 

### is easy 

### to verify that gear bias 

### terms cancel out of Eq. (V-10) so that the factor

### A is freeof gear bias. Using the definitions 

### of w U 

### and wL presented on p. 

### V-18, 

### Eq. (V-10) can be rewritten 

### as:

where 

### DpU 9 

### DW’ DPL, and DRL are the actual densities of organisms 

### in the upper and 

### lower layers of the 

### river and intake bay. 

### Next, we 

### express 

### w

### L as a function of 

### w U and 

### A:

(v-11)

As discussed in Section 

### 2.2.1, we assume that there are no lateral trends in ichthyoplankton abundance 

### inthe upper layer 

### of the river, and 

### no active avoidance of 

### the intake structure by entrainable organisms. Given those assumptions, 

### and given that 

### all

### water in the upper 

### layer of an intake bay 

### is drawn from the upper layer 

### of the river, the density of organisms inthe upper layer of 

### an intakebay should be equal to the density inthe upper layer 

### of the 

## * river. Therefore, the actual value of 

### wu, which we 

### willdenote 

### as wu, should be equal to 

### 1.0 (observed deviations from 

### 1.0 are assumed tobe attributable to 

### sampling error or to unequal

# * plant and river gear efficiencies). Assuming that the value of 

### wv 

### is 

### 1.0, our estimate of the true value of 

### w

### L is given by:
(v-12)

### V-27

Values 

### of 

### A are computed from the seasonal estimates 

### of upper and lower 

### layer w-ratios [Eqs. 

### (V-8 and 

### (V-911. Wherever possible, 

### we have chosen 

### to combine estimates 

### of 

### A obtained 

### at the 

### same plant during 

# * different years before computing

### wLo We do this for two reasons: 

### (1) If the observed value 

### of

### wu is 0.0, 

### as occasionally occurs for 

### some species and life-stages, then unless the

# *observed value of w

### L is also 

### 0.0, 

### A is equal 

### to -1.0 and 

### w

### L (= 2/01 cannot be computed. Combining values 

### of

### A across years eliminates this problem. If so 

### few organisms 

### are collected at a given plant that observed values of 

### wu are equal to 0.0 for more than one year, the 

### GBC method 

### is not used to calculate a W-factor

# . (2) Combining values of A across 

### years allows W-factors 

### tobe computed 

### for years 

### inwhich the plant data 

### are not usable (e.g., Rosetorr in

### 19741, provided that sufficient river data 

### exist for that year and that sufficient plant data 

### exist 

### for other years. Thus, 

### estimates of A thzt apply across years 

### at any given plant are computed 

### as unweighted 

### means 

### of the values computed for each individual year 

### (3years at Indian Point, 2 years for all 

# * other plants). After the across-years estimate 

### of

### A is obtained, 

### w

### L is calculated using 

### Eq. (V-12). 

# * Finally, the computed value 

### of 

### w

### L and the fractional distribution of organisms 

### inthe upper and lower 

### layers of the river


### are used to compute the W-factor. 

### If power plants withdrew water equally from 

### all depths and there 

### were no vertical stratification

### of organisms inthe river, then the number of organisms entrained per unit 

### time could be estimated from the following equation:..."



## v-28

where

### E = number 

### of crganisms entrained per unit time, Q 

### =

### water withdrawal rate, W 

### = depth-averaged W-factor N 

### =number 

### of organisms 

### inthe river segment from which the plant withdraws water, 

### V = volume of the river segment from which 

### water is withdrawn.

Since we have assumed that the river consists 

### of 

### two distinct layers and that 

### power plants draw water differentially from these two 

### layers, 

### Eq. 

### (V-13) must be modified to account for these assumptions:

where 

## Qu, QL = water withdrawal rates from the upper and lower layers 

### of the 

### river, 

# ** "ut wL = upper and lower layer w-ratios,



### NU, NL numbers of organisms inthe upper and 

### lower layers 

### of the segment from which water 

### is withdrawn,



### VvyVL volumes of upper and lower layers 

### of the river segment from which water 

### iswithdraw;.

-'_ Because we have defined the upper' and lover layers of the river with respect to a boundary that 

### is equidistant between surface and bottom from shore to shore, 

### Vu is equal 

### to 

### VL. Given that 57%of the cooling water is drawn from

# *the upper layer of the river and 

### 43% from the lcwer layer, and that wu 

### is equal to 1.0, 

### Eq. 

### (V-14) can 

### be rewritten as:

r-V-29



### If we define 

### f

### U as the fraction 

### of organisms inthe river 

### segment that 

### are contained inthe upper layer 

### of the river and f

### L as the fraction contained inthe 

### lower layer, then: 

## * E = 1.14QfUN/V + 0.86Qw,fLN/V (V-16) 

# *
= Q(1.14fU + 0.86wLfL)N/V 

# .
If Eqs. (V-13) and (V-16) 

### are compared, it can be seen that the expression 

### inparentheses inEq. (V-16) 

### is equivalent to the depth-averaged W-factor inEq. (V-13). Therefore,



## w 1.14fU + O.86wLfL 

### (V-17) 

# *

Since we have defined the upper 

### and lower layers 

### of 

### the river so that their volumes 

### are equal, only the densities 

### of organisms 

### inthe upper and 

### lower 

### layers, rather than absolute standing crops, 

### are requited inorder to estimate 

### fU and fL. These values 

### are obtained from the same databases used to compute the w-ratios and theMU W-factors. Seasonal upper and lower layer densities are computed by the 

### same method used incomputing the w-ratios [

### Eqs. (V-2) and (V-311 However, data obtained on 

### a11 river sampling dates areused, regardless 

### of whether or not simultaneous collections 

### were made at the plant. Given the seasonal mean densities D

### RU and 

### DRL, the fractional distribution 

### of organisms in the upper and 

### lower layers 

### of the 

### river is calculated from the following equations:

(v-19)

Unlike the values 

### of 

### A, fU and fL are computed separately for each year. Thus, each 

### GBC W-factor 

### is computed from a value of 

### A that applies across years and values of fU and fL that 

### are year-specific.r_ 

### V-30

3. RESULTS

The results obtained when the MU and GBC methods 

### were applied to plant and river ichthyoplankton data collected at the Bowline, Lovett, Indian Point, Roseton, and Danskarmner power plants arepresented in Tables 

### V-3 

# - V-12. The populations for which W-factors could be computed 

### are striped

### bass (Tables V-3 and V-41, white perch (Tables V-5 and V-61, Atlantic tomcod (Tables V-7 and 

### V-81, bay anchovy (Tables V-9 and 

### V-101, and 

# - Alosa spp. (Tables V-11 and 

### V-12). Appendix 

### B contains descriptions of the data bases used to compute W-factors for each 

### plant and year. Separate day and night W-factors 

### were computed for each of four lif e-stages (eggs

# ,yolk-sac larvae, post yolk-sac larvae, and juveniles) for the years 1974 through 1976. It 

### was not possible 

### to compute W-factors 

### for allpopulations, life-stages, plants, and years. 

### In some 

### cases 

### no data

### were available. 

### No 1974 data 

### for any population 

### were available for Lovett and Danskamner. Although 1974 ichthyoplankton abundance data were available for all species for Bowline and Roseton, only the striped bass 

### data
were broken down by life-stage. For allother populations yolk-sac larvae, post yolk-sac larvae, and juveniles 

### were pooled. W-factors could not be computed for those populations and life-stages. For bay anchovy, 

### 1976 Lovett intake data were not available. 

### In other cases W-factors were not computed because the available data 

### were deemed unsuitable 

### (see Section 4) 

### or because too few organisms


### were caught. The 1974 stripedbass data collected 

### at the Roseton intake

' 

### were excluded because 

### of insufficient sampling effort (see discussion inSection

### 4). Therefore, no MU W-factors could be calculated. Since sufficient Roseton/Danskauaner river transect data 

### were available for 1974, CBC W-factors could 

### stillbe calculated. 

### As mentioned in Section 

### 2.2, GBC W-factors were not calculated for any population at Bowline because this plant does not draw water directly from the river. 

### Mu W-factors for 

### Bowline were calculated using the Bowline Pond data rather than the intake data to estimate tiredensity of organisms at the plant. Unlike the intake samples, the pond samples 

### are collected using J

,'

I -

,-

# -- 

# --

--

--

--



# -- --

--



# --
--

-- 

# --

-- 

# --

--

--



# --



# -- --

--



# --

-- V-31



### Table V-3. 

### W-factors 

### for striped bass computed 

## using theModified Utility (W)

### methodasb

1974 

### P1

## ant Life-stage 

### Day Night
Bowline Eggs 

### YSL 

### PYSL 

### Juveniles Lovet

## t Eggs YSL 

### PYSL Juveni

### 1es 

### Indian. Eggs Poi

### nt YSL 

### PY

## SL Juveni

### 1

## es Roseton Eggs YSL 

### PYSL 

### Juveni1es 

### Danskamner Eggs YSL 

### PYSL 

### Juveni

### 1

## es
aSources of 

### data 

### described 

### inAppendix 

### B. 

### 1975 1976 

### Day Night 

### Day Night



### 0.03 0.09 0.28 0.00 

## 0.32 0.11 

## 0.10 0.17 0.24 0.98 

### 0.10 

## 0.60

0.98 0.16 0.28 

### 4.16 

## 0.43 0.14 0.55 

### 1.42 3.31 0.04 0.64



### 0.64 1.94 0.49 0.85 

### 0.41 0.28 0.48 

### 0.49 

### 0.69 1-40 1.07 

### 0.39

c

### 1.95 

### 2.01 

### 3.31 1.66 1.03 1.05 0.76 2.20 

### 0.61 0.91 0.29

0.39 1.56 6.82 0-61 0.48 

### 2.58 

### 0.78 

### 1.23 0.16 

## 2.34 1.03

bThe 

### available

### data were 

## not sufficient 

### tocalculateW-factors 

## for all

life-stages 

## at all plants during all

### years.

# -- -- 

# -- 

# --

--

-- 

# -- -- 

# -- -- -- 

# -- --



# --

-- -- V-32



## Table V-4. 

## W-factors for striped 

## bas computed using 

## the 

## Gear Bias Cancelling (GBC) 

## rnethoda~g~C

1974 1975 1976 P1

## ant 

## Life-stage Day Night Day Night 

## Day Night


## Lovett Eggs -.I 

## 1.15 0.32 0.59 0.32 YSL 

# -.. 1.16 1.15 1.16 PY

## SL -.I 

### 3.37 1.45 3.29 

## 1.55 

## Juveniles -.c 

## Indian Point Eggs 

## YSL 1.22 0.82 0.16 0.80 

### 1.20 0.77 0.34 0.80 

## 1.20 0.79 

## 0.24 0.86 PY

### SL 

## Juveni

## 1es 0.98

I

### 0.97 

### 0.98

-0 

### 0.91 

# -_ 0.97 0.91

0- 

## Ros

## eton Eggs YSL 

### 0.66 0.98 0.37 

## 0.78 0.37 0.95 0.78 0.41 

### 0.94 0.24 

### 0.76 PY

## SL 0.55 

### 0.84 0.64 

### 0.88 0.62 0.86 

## Juveni

## 1es 

## Danskamner Eggs 

### 1.18 

### 1-17 0.94 YSL -* 

### 0.55 0.88 

### 0.52 

### 0.86 PYSL -0 

### 0.69 

### 0.99 0.68 0.99 

## Juveni

### 1

## es -0 

## aSources of

## data described in

## Appendix B. bThe 

## availabledatawere not 

## sufficient 

## toczlculateW-factors for 

## all

life-stages at all

## plants during 

## allyears. CGBC

## W-factors arenot 

## calculatedfor 

## Bowline.

r-

# -- 

# -- -- 

# --

--



# -- 

# --

-- -- --
-- 

# --

--



# --

-- 

# --

--

--



# --



# -- -- 

# --

--



# --



# --

-- v-33



### Table V-5. W-factors for white perch computed usingthe Modified Utility

### (MU) methodasb

Plant Lif 

### e-stage

Bow1

### ine Eggs YSL PYSL 

### Juveni1

### es Lovett 

### Eggs Y

### SL 

### PYSL Juveni1

### es Indian 

### Eggs Point YSL 

### PY

## SL Juveni

### 19s 

### Roset

## on Eggs YSL 

### PYSL 

### Juveni

### 1es Dan

## skamer Eggs YSL 

### PYSL Juveni1es 1974 1975 1976 Day Night Day Night Day Night



### 21

# .51 2.04 c

### 0.78 0.49 

# .­ 0.09 0.74 0.24 0.78


### 0.67 0.37 0.21 0.14 1.12 1.17 

### 0.03 0.13
2.59 

### 2.19 6.21 0.81 0.16 1.30 

### 0.14 0.60 

### 0.50 1.28 1.10

-0 

### 4.38 

### 3.23 9.56 4.93 0.85 1.34 0.22 

### 0.56 0.

### a4 0.88 

### 1.98 0.40


### 16.68 11.14 18.67 

### 16

# . 68 0.51 

### 0.00 0.21 0.32 1.85 1.04 0.45 0.83

aSources of data described 

### inAppendix 

## B. bThe available data

## were not sufficient 

### tocalculate W-factors 

### for all

life-stages at allplants during allyears.

# -- -- -- 

# --



# -- 

# --

--

-- 

# -- 

# -- 

# -- v-34



### Table 

### V-6. W-factors for white perch computed using the Gear Bias Cancel

### 1ing (GBC) methodasb9c



### 1974 

### 1975 1976 

## P1ant Life-stage 

### Day Night Day Night Day Night

Lovett 

### Eggs 0.45 1.12 YSL 

# _- 0.75 0.73 PY

## SL 1.12 0.98 1.13 0.98 Juven

### i1

### es -I 

### Indian Eggs 

### 1.18 1.43 

### 1.18 1.73 Point YSL 

### 0.89 0.53 0.93 0.69 PYSL 

### 0.90 0.76 0.93 0.80 Juveni

### 1es Roseton Eggs 

### 0.43 0.56 0.50 

### 0.54 Y

## SL 0.28 1.46 0.64 1.33 PY

## SL 1.05 0.99 1.05 0.99 Juveni1es -0 

### Danskamner Eggs 

### 0.35 

### 0.44 0.43 0.53 YSL 

### 0.69 1.06 0.88 1.09 

### PYSL 

### 0.82 1.01 0.80 1.02 Juveni

### 1es

I J 

### aSources 

### of data described 

### inAppendix 

## B. bThe availabledatawere not sufficient tocalculate 1-factors for all

life-stages at 

### all plants during

### allyears. CGBC 

### W-factors are not calculatedfor Bowline.



# .

# -- --



# -- --

-- v-35



### Table 

### V-7. W-factors 

## for Atlantic tomcod computed usingthe Modified Utility 

## (MU)methodasb



### 1974 1975 1976 P1

### ant Life-stage Day Night Day 

### Night 

### Day Night
Bowline E9gs YSL 

### PYSL Juveni

### 1es Lovett Eggs YSL PYSL Juven

### i1

### es 

### Indian Eggs 

### Poi

## nt YSL PY

## SL Juven

### i

### Ies 

### Roseton

. 

### Eggs YSL 

### PYSL Juveni

### 12s 

### Dan

## skamner Egg5 

### Y

## SL PYSL Juveni

### 1es 0.40 

### 0.50 0.41 

### 1.09 0.00 

### 0.00



### 0.42 0.71 1.27 

### 1.70 0.00 0.04


# _­ 0.70 

### 10.34

aSources 

### ofdata described inAppendix 

### B.

I,- . 

### bThe 

### available data were not sufficienttocalculateW-factors for all life-stages at allplants during 

### allyears.

# -- 

# --

-- -- 

# -- -- --

-- 

# -- V-36



## Table V-8. 

## W-factors 

## forAtlantictomcod computed using

## the Gear Bias Cancel1ing.

## (GBC)rnethoda9bsc

P1

## ant Life-stage
Lovett Eggs YSL 

## PYSL Juveni

### 1

## es Indian 

### Eggs 

## Point YSL PY

## SL Juveni

### 1

## es Roseton Eggs YSL 

### PYSL 

## Juveni

## 1es Danskamner Eggs YSL 

### PYSL 

## Juveni

### 1

## es 1974 1975 

### Day Night 

## Day Night

0­ 

### 1.32 1.03 1,08 0.66 0.08 

### 0.10

-0 

# .. e­

-I

-0 0­ 

### 0.32 0.57 

### 0.42 0.55 1976 

### Day Night



# -. 1.28 1.00 1.08 0.53 

### 0.08 0.08

I­

-0 

### 2.04 

### 0.54 ”-..

% /

~



## aSources of data described in

## Appendix 8. 

## bThe available data were not sufficient

## tocalculateW-factors for 

## all

life-stages at all

## plants during 

## allyears.
CGBC

## W-factors are not calculated for 

## Bowline. ..i

# -- 

# --
--

-- 

# -- -- 

# --

-- v-37



### Table V-9. 

### W-factors for

### bay anc ovy computed usingthe Modified 

### Uti1ity

### (W) methodas

# I:

P1

### ant Life-stage


### Bowline 

### Eggs YSL PYSL 

### Juveniles 

## Lovett Eggs YSL PYSL 

### Juveni1

### es Indian Eggs Point YSL 

## PYSL Juveni

### 1

### es Roseton 

### Eggs YSL PYSL Juveni

### 1es Danskamner Eggs YSL 

## PYSL Juveni1

## es 1974 1975 1976 Day 

### Night Day Night 

### Day Night
0.11 

### 0.31 0.01 0.57 0.15 0.95 

### 0.98 0.24 0.51 0.78 1.77 7.11 4.31 44.35 

### 225.35

1.97 

### 3.65 0.17 0.45 0.81 1.26 0.90 2.24 9.19 

### 1.65 1.39 0.12 0.67 

### 0.41 1.45 0.63 0.97 1.60 

### 2.03 

### 0.20 

### 1.27 36.32 1.61 10.53 

### 1.05

aSources of data described inAppendix 

### B. 

## bThe available

### data were not 

### sufficient tocalculateW-factors for all

life-stages at 

### allplants during

### allyears.

# -- --

-- Table V-10. 

## W-factors for bay 

## anchovy computed using

## the Gear Bias Cancel1i

### ng (GBC) 

## methodasbJ



### 1974 1975 1976 

### P1

## ant life-stage Day Night 

### Day Night 

## Day Night


## Lovett EW YSL 

### 0.38 0.49 0.06 156.94 

### PYX 1.34 1.06 

## Juveni

### 1

## es 7.83 1.20 

## Indian Eggs 0.53 0.33 0.53 0.19 0.60 

### 0.16 

### Point YSL 

### 1.43 1.35 

### 0.30 1.40 0.27 PY

## SL 1.01 0.58 0.96 

### 0.63 0.98 0.60 

## Juveni

### 1

## es 0.31 0.74 0.68 

### 0.85 0.86 Rose

## t

## on Eggs 

### YSL -e 

### PY

### SL 

## Juveni

## 1es .,

" 7 

## Danskamner Eggs YSL 

### .PYSL 

## Juveni1

### es


## aSources of 

### data 

## described in

## Appendix 6. 

## bThe availabledatawere not sufficient.to

## calculateW-factors for all



## life-stages at all

### plants during all

## years. CGBC 

### W-factors 

## are not calculated for

## Bowline.

# -- 

# --
--



# -- 

# -- --



# --

-- -- -- 

# --



# --

--



# -- v-39



### Table V-11. 

## W-factors for AlGsa

## sp. 

## computed using the

## Modified Utility (MU)

## rnZFiZas

# g

Plant Lif

## e-stage

PYSL 

## Juveni1

## es Lovett 

## Eggs YSL PYSL

.. 

## Juveni

### 1es Indian 

### Eggs Point YSL 

### PYX 

## Juveniles Roseton Eggs YSL 

### PYSL 

## Juveni

### 1es 

## Danskamner Eggs YSL 

### PYSL 

## Juveni

### 1es ~ ~~ ~~ ~ ~~~~ ~- ­ 

### 1974 1975 

### 1976 

## Day Night 

## Day Night 

## Day Night

-I -I 

### 0.44 

## 5.33

-0


### 0.00 -e 

### 2.26 1.65

.



### 0.79 0.50 1.28 1.49 1.36 1.47 1.05 1.82

0- -.I -0 

### 12.19 1.53 10.07 

### 5.82



### 1.18 0.91 0,63 0.96 

### 0.55 9.40 

### 0.34 0.66 1.33 0.43 

### 1.97 62.24 

### 22

### , 23 

### 17.77 52.67 2.07 1.32 

### 0.46 

### 0.76 1.12 0.40 

### 0.53 0.69 0.71 

### 0.17 

### 0.38


## aSources of data described in

### Appendix 6. bThe 

## availabledatawere not sufficient 

## tocalculateW-factors for 

## all

life-stages at allplants during all

## years.

### --

--- 

### V-40



## TableV-12. W-factors forAlosa spp. 

## computed using 

## the 

## Gear Bias Cancel1i

### ng 

## (G6~thoda~b~C

1974 1975 1976 

## PIant Life-stage Day Night 

### Day Night 

### Day Night


## Lovett EXIS 

### YSL PYSL 

## Juveni

### 1

## es Indian Point Eggs 

## YSL 

# -- -- 0.86 0.69 

# -- 

# -- 0.89 0.59 

# -_ 

# -- 0.92 

### 0.69 PY

## SL Juveni1es 

### 1.02 0.91



# -- -- 1.06 0.90



# -- 

# -- 1.05 0.89



# -- 

# -- Roseton Eggs YSL 

### 0.48 0.77 0.78 0.84 0.51 0.96 

### 0.87 0.78 PY

## Sl 0.92 0.88 

### 0.93 

### 0.88 

## Juven5

## 1es 0.53 0.85 

## Danskanmer Eggs YSL 

### 0.47 0.89 

### 0.57 0.74 0.50 1.02 

### 0.70 0.66 

### PYSL 

## 1.16 0.98 1.16 0.97 Juveni

### 1es 

### 0.92 

### 1.23


## aSources of 

## data described inAppendix 

### B. 

## bThe availabledatawere not sufficientto‘calculateW-factors for all

life-stages at allplants during

## allyears,
CGBC 

## W-factors are

## not calculatedfor Bowline. r-.

,d

”...

r­

..

,

### V-41

the 

### same gear and deployment technique used to collect the river samples. Therefore, 

### MU W-factors computed from the pond data should be free 

### from gear bias. W-factors computed using any method willbehighly unreliable if they 

### are based on ichthyoplankton collections containing only a few organisms. Unrealisticallyhigh or 

### low W-factors (particularlyvalues of 0 

### or infinity) 

### are more likely tobe produced under these circumstances. Therefore, we used 

### an exclusion criterion to 

### eliminate W-factors based on insufficient numbers of organisms. 

### In order to calculate a W-factor 

### for 

### a given population, life-stage, and period (day 

### or night) at a givenplant, 

### we require that at 

### least 10 organisms have been collected ineither the plant samples 

### or the river samples used to compute thatW-factor. Many data sets failed tomeet the 10-organisms exclusion criterion. 

### In particular, inonly 

### a few cases, primarily for bay anchovy and Alosa, was it possible to compute W-factors for entrainablejuveniles. So few data sets for juvenile striped bass and white perch satisfied the 

### 10-organism criterion that


### we decided not to compute any 

### MU or 

### GBC W-factors 

### for juveniles 

### of these species. When near-field data collected at parer plant intakes and river transects were unavailable or unusable (Section

### 41,W-factors were computed from riverwide abundance data using the method described in Chapter 

### V-6.

### V-42

4. DIFAGREEMENTS WITHDATA 

### AND METHODS USED BY LMS IN

### EXHIBITUT-3 TO COMPUTE W-RATIOS
Ideally, plant river samples used to compute w-ratios or W-factors should be collectedusing identical gear deployed in

### an identicalmanner (Carpenter 1979). Moreover, large numbers of samples should be collected 

### at each location inorder to obtain

### estimates of plant and river abundance that 

### are as precise as possible given the limitations imposed by the patchy distribution

### of ichthyoplankton. Judged by these 

### criteria, none of the Hudson River sampling programs has every been ideal 

### for obtainingdata suitable for estimatingW-factors. For 

### the most part 

### we have used the available data inspite of their limitations. However, 

### some of the data have been collected under conditions that deviate so 

### far from the ideal that 

### we believe they should not be used to compute W-factors. In this sectionwe identify these data and explain our reasons for excluding them. We also discuss 

### two methodological problems that 

### we believe have led 

### LMS tounderestimate the numbers 

### of organisms entrained at Hudson River power plants. One 

### of these 

### is the overestimation 

### of sample volumes at the Indian Point intake and discharge staions. The other 

### is LMS' assumption that if no organisms 

### are collected ineither the plant 

### or the river samples, the w-ratio 

### is equal to zero.

4.1 NINETY MINUTE 

### SAMPLE DURATIONS 

### AT 

### THE BOWLINE INTAKE According topage 9.1A-15 of Exhibit UT-7A, during the entire 

### 1974 entrainment sampling season and prior to June 10, 1975, the sampling duration at the Bowline intake was 90 minutes. Ecological Analysts (l976a) conducted studies 

### at Bowline and Lovett between May and July 1975 inorder todetermine whether nets left inthe

### water for such 'extended periods 0.f

### time could become clogged with detritus. 

### It was found that such clogging could indeed occur, and could result in substantial reductions inthe filtration efficiency 

### of the nets. Sample volumes at Bowline are not measured directly from flowmeter readings, but are estimated indirectlyusing the matrix 

### of intake

### v-43

velocities presented inTable 9.1A-4 of Exhibit UT-7A. Therefore, in addition to possible increased gear avoidance, reductions 

### in filttation efficiency due 

### to clogging would cause 

### samplevolumes at the Bowline intake 

### tobe overestimated. If intake sample volumes used 

### in computations are erroneously high, then the densities of organisms in the intake samples (and therefore the 

### w-ratios) would be erroneously 

### low. According to 

### EA's 1975 Annual Interpretive Report 

### for Bowline (Ecological Analysts 1976a, p. 

### B-21, significantreductions innet filtrationefficiencydue to clogging

### were observed inexperiments conducted on May 7, May 14, June 2, June 25, and July 10, 1975. Clogging was most frequentlyobserved innets placed at the bottom of the intakebay. In 

### some of theseexperiments, clogging began tobe observed after only about 

### 40 m3 of water had been filtered; 

### in all experiments inwhich significant clogging was observed,' 

### it was observed before 80 

### m3 had been filtered. 

### The importance 

### of this finding can be seen from Table V-13, which contains inforatation pertaining to the volumes 

### of samples collected at the Bowline intakeduring the period between June 5 and July 

### 12, 1974 (no intake samples collected prior 

### to June 5 were used to compute w-ratios). The volumes of intake samples collected during this period, as estimated using Table 9.1A-4 

### of



## . Exhibit UT-'IA, are 

### far higher than the 40 to 80m' range inwhich 

### EA began 

### to observe clogging. 

### The mean sample volumes on the four dates listed 

### in Table 

### V-13 ranged from 132 

### to 

### 148 m3



# . The volumes of the 

### 3 smallest samples collected during 

### this period were larger than 130

### m 

# . Based 

### on EA's observations of clogging 

### zt sample volumes 

### of 80 

### m

### 3 and less, such clogging could have occurred frequently at the Bowline intake in 1974. According to the testimony 

### of Dr. Gerald Lauer 

### of 

### EA the clogging of nets observed 

### at Bowline 

### in 1975was 

### an unusual occurrence related to 

### "an extraordinary increase 

### in the volume of detritus collected inthe nets" (Transcript pp. 8119). Under cross-examination 

### he claimed that this conclusion was included in

### EA'S report. Dr. Lauer stated (Transcript pp. 8119-20): "as I recall from the report itself, in general the report concluded that based upon these perceptions of

### v-44



## Table V-13. 

## Mean, minimum, 

## and maximum volumes of 

## samples collected at the Bo 

## line intake

## on selected sampling 

## dates during 

## 1974a~ 

# 1

~~ 

## Volume (m3) 

## Date Mean (number of 

## samples) Minimum 

## Maximum


## June 5 (day) 

## 142.5 (12) 131.4 163

# .8 

## June 5 (night) 

## 132.3 (12) 131.4 134.1 

## June 12 (day) 

## 136.1 (9) 

## 131.4 163.8 

## June 12 (night) 

## 142.5 (6) 131.4 163.8 

## June 19 (day) 

## 145.9 (18) 131.4 163.8 

## June 1-9(night) 

## 137.4 (12) 131.4 163.8 

## June 26 (day) 

## 142.5 (18) 131.4 163.8 

## June 26 (night) 

## 147.6 (12) 131.4 163.8 

## July 2 

## (day) 142.5 (12) 131

# .4 163.8 

## July 2 (night) 

### 140.6 

## (11) 131.4 16318

- -

~~ ~ ~ ___ ~ ~~ 

## aSource of

### data 

## described in

## Appendix B. 

### bData 

## collected on these dates were used by 

### LMS 

## to

## compute

w-ratios for

## striped bass 

## life-stages.

### v-45

volume 

### of detritus that you 

### see in the nets ordinarily, this

### was an extraordinary occurrence that you would not expect to 

### see most of the 

### time inmost 

### years." Despite a thorough search, 

### I found 

### no such conclusions anywhere in

### EA'S report on the clogging experiments (Ecological Analysts 1976a, pp. B-1 

# - B-9). Moreover, Ecological 

### Analysts didnot conduct the entrainment sampling 

### at Bowline 

### in

### 1974 (it

### was done by 

### LMS), so that neither 

### Dr. Lauer nor any other

### EA personnel could have observed whether detritus loads at the Bowline plant 

### were 

### lower 

### in 1974 than 

### in 1975. In any 

### case, Dr. Lauer's suggestion that the occurrence 

### of clogging
can be determined from the amount 

### ofvisible detritus collected in the nets 

### is at variance with the conclusion 

### of Smith, Counts and Clutter (1968; p. 245) that:

We found that indirectmeans 

### of judging whether clogging willtake place, such as taking a Secchi disc reading, was not a suitable substitute for monitoring filtration efficiency. Inspection of the net after a 

### tow was not reliable for determining whether ithad clogged. After heavy clogging 

### at San Pedro, the net 

### was discolored but no indication

### of clogging appeared 

### at the Catalina Island site, even when the net had been accepting 

### less than half 

### of the 

### water presented to it at the end 

### of the tow.
According these authors, clogging can be detected only 

### by comparing velocity readings taken from flcwmeters placed inside and outside a plankton net. Since there 

### is no scientificallyvalid evidence that the amount of detrituspresent 

### at the Bowline plant was higher in 

### 1975 than in 1974, and since subjective impressions about the amount 

### of detritus collected ina plankton sample are not alone sufficient to determine whether or not significant clogging has occurred, we conclude that cloggingmay 

### wellhave occurred during the 1974 sampling program and gone undetected. Therefore we believe that all of the data collected 

### at the Bowline intake during 

### 1974 and up to June 10, 1975 should have been excluded when 

### LMS' w-ratios were calculated. We have not used any'of this data to compute W-factors. 

### The sampling program conducted 

### at Lovett in 

### 1975V-46



### was identical to that at Bowline inthat the duration of sampling 

### at _- the intake

### was 

### 90 minutes on all sampling dates prior to 3une 10, 1975 (Ecological Analysts 1976b). 

### We have excluded this data from our 

### estimates of W-factors for all species except Atlantic tomcod. This exception was made because Lovett and Bowline 

### are the only plants at which enough simultaneous plant/river sampling 

### is conducted during the early spring forW-factors tobe calculated foryolk-sac larvae 

### of this species. 

### We felt that the advantage 

### of having the additional data for this life-stage outweighed the disadvantage 

### of the potential bias.



### 4.2 BOWLINE 

### POND VS 

### BOWLINE INTAKE When the Bowline plant 

### is operating, Bowline Pond functions 

### as a very large intakebay: water is pulled into the pond by the pumps, entrained through the plant, and discharged back out intoBaverstraw Bay. Organisms drawn inwith the cooling 

### water can leave the pond in only two ways: by being entrained through the plant or by swimming back out of the pond against the current caused by the intake flow. In order to avoid being entrained, an organism must first locate the pond inlet, a "keyhole" only 

### 240 feetWide and about 15 feet deep (Exhibit. UT-!, Fig. 3.1-6). Judging from Fig. 3.1-7 of Exhibit UT-7, the pond itself 

### is roughly 1500 to 2000 feet across and 30 to

### 40 feet deep. Having reached the inlet, the organism must then swim out against the flow being drawn into the pond. It does not 

### seem likely that very many larvae could achieve this featbefore being entrained (escape 

### is, 

### of course, out 

### of the question for eggs). If all, 

### or nearly all, the ichthyoplankton drawn into Bowline Pond 

### is destined tobe entrained through the plant, it seems reasonable to us to use the data collected inBowline Pond, rather than data collected at the intake, to calculateW-factors. 

### As we mentioned in Section 2.2 and again in Section 

### 3, the pond samples are collected with the same gear used to collect the river samples, and thus no gear biases should be introduced when w-ratios 

### are calculated using pond data rather than intake data. LMS did calculatew-ratios using pond

### v-47

data, but 

### only when no intake data was available. 

### We believe that the pond data 

### are clearly superior to the intake data and that they should have been used 

### to calculate 

### all of the w-ratios for Bowline.



### 4.3 

### INSUFFICIENT SAMPLING 

### EFFORT AT THE ROSETON 

### PLANT, 1974 

### The sampling program conducted at Roseton in 1974

### was of especiallypoor design for estimatingW-factors. Only 12 samples 

### were collected 

### at the intake on each sampling date. 

### Of these, 

### six were collected at dawn 

### or dusk. However, 

### LMS didnot use dawn 

### or dusk data to compute w-ratios. Thus, half 

### of the samples collected at the Roseton intake

### were automatically excluded from 

### LMS' analysis. 

### Of the remaining six samples per date, many failed to

### meet LMS' outlier criteria (Exhibit UT-3, p. 3-IV-49) and were therefore excluded. The remaining sampling effort, measured either as the number of samples collected 

### or as the total volume sampled, was extraordinarily small. Table 

### V-14 contains the total number of day and night samples collected (minus outliers) and the total day and night sample volumes for five sampling dates between May 

### 9 and July 2, 1974. Data collected on these five dates wereused by LMS to compute all 1974w-ratios for Roseton (Exhibit 

### UT-3, p. 3-IV-51). After excluding outliers, dawn and dusk samples, 

### and samples collected on days when no river samples were collected, 

### only 17 samples remain for the entire stripedbass entrairiment season. Only eight samples were available 

### for computing, daytime w-ratios and only nine for nighttime w-ratios. On three 

### of the five dates the totalvolume sampled during 

### the day 

### was 

### less than 20 

### m3

### ;on two dates less than 20 m

### 3 was sampled 

### at night. 

### The highest volume sampled, 

### on the night 

### of 

### July 2, 

### was 

### less than 120

### m3


# . By comparison, the smallest individual sample collected atBowline 

### 3 between June 5 and July 12, 1974had a volume greater than 130m (Table V-13). Tables 

### V-14 throagh V-16 contain similar analyses of the sampling effort 

### at Roseton 

### in 1975and 

### at Bowline and Indian Point 

### in both 1974 and 1975. The data used by 

### LMS to compute w-ratios for Bowline and Indian Point in 1974, and for all three plants in 1975, were obta'ined

### V-48



### Table V-14. 

## Numbers of 

### samples and 

## total volumes samttled atthe 

## Roseton intake'

## on selected sampling 

## dates during 1974 and 

### 1975a~b

_- 

### Volume' 

### (number of 

### samples) 

## Date Day Night


### 1974 May 9 

### 10.0 

### (1) 8.5 (1) 

## May 21 69.7 

### (3) 46.7 (2) 

## June 4 

### 0 

## (0) 15.6 (1) 

## June 19 63.5 

### (3) 71.4 

### (2) July

### 2 18.8 (1) 117.9 

### (3) 1975 May 15 420.9 (6) 259.7 (3) 

### May 22 406.6 (6) 205.5 

### (3) May 29 748.6 (8) 

### 410.6 (5) 

## June 2 251.9 (18) 

### 126.9 (8) 

## June 9 272.0 (6) 125.7 (3) 

## June 19 292.8 (6) 128.6 

### (3) 

## June 23 261.5 (6) 147.5 

### (3)
aSource of 

### data described in

### Appendix B. 

### bData 

## collected 

## on these dateswere 

## used by LMS 

## tocompute

w-ratios for 

### striped bass life-stages. CSum 

### of

### volumes of 

### all 

### samples collected during the 

### day 

### or night,



### after exc1ud

### ing 

### out1i

## ers

# .

## v-49



## TableV-15. 

## Numbers of

## samples and 

## total 

## volumes sampled 

## atthe Indian Point intake and 

## discharge 

## stations on selected sampling 

## dates during 

### 1974 and 

## 1975a~b9c

Date May 7 

### May 28 

## June 4 

## June 13 

## June 25 

## July 2 

## July 9

. 

### 1974

## Vo1

## ume (number of 

## samples) Date
279.9 (23) 

### 258.5 (18) 519.6 (36 425.0 

### (27) 473.2 

### (34) 663.6 (43) 

### 476.0 (33) 

### May 13
M3y 

### 20 

### May 

### 27 

## June 3 

## June 10 

## June 

## 17 June 24 

## July 1 1975Vo

### 1

## me (number of 

## samples)
179.8 

### (22) 

### 545.5 (92) 470.0 (72) 448.1 

### (72) 379.5 (54) 

### 608.6 (96) 869.1 

### (140) 555.8 (88)


## aSource of

## data described in

## Appendix 6. 

## bData collected

## on these dates were used by 

### LMS 

## to

## compute w-ratios

for 

## stripedbass 1

## ife-stages.
CAll 

## intake and 

## discharge stations

## combined. dSum 

## of volumes of 

### all 

## samples and 

### total 

## number of 

## samples ccllected

during

## nighttime 

## at all 

## intake and discharge stations 

## (no plant 

## samples are collected during

## daytime), after excluding outliers.

### V-50 ,.~ ".



### Table V-16. 

### Numbers of 

### samples and total 

### volumes sampled at 

### the Bowline 

## intakeand 

### in Bowline Pond 

## on selected sampling dates during 1974 

### and 1975asb



### Yo1

## ume (number of 

### samp1es)
_- 

## Bowline intake Bowlinepond 

## Date Day Night 

## DaY Night -.



# - May 

### 8 

## 0 (0) 

## 0 (0) 1425.5 (6) 1572.7 (10) 

### May 22 

### 0 (0) 

### 0 (0) 1225.8 (8) 

### 2048.7 (10) 

## June 5 1710.0 

### (12) 1587.6 (12) 1185.1 

### (7) 2261.9 

### (10) 

### June 19 2626.2 (18) 1648.8 (12) 1285.3 (8) 1758.8 (10)

' 

### June 26 2565.0 (18) 1771.2 (12) 217.9 (4) 

### 0 (0) 

### July 2 1710.0 (12) 1546.2 (11) 

### 1946.2 (10) 

### 752.8 

### (3) 1975 

### May 20 

## 0 (0) 0 (0) 510.1 

### (2) 558.4 

### (2) 

### May 27 

## 0 (0) 0 

## (0) 575.0 (2) 602.0 (2) 

### June 3 333.9 

### (3) 683.1 (6) 

### 570.2 (2) 

### 529.0 (2) 

## June 10 50.3 

### (8) 92.7 (15) 586.7 

### (2) 612.7 (2) 

### 1974



# -

June 17 92.7 (15) 37.1 (6) 618.5 (2) 563.2 (2) 

### June 18 30.9 

## (5) 117.4 (19) 

## 0 (0) 0 (0)

-~~­ 

### aSource of

### data described in Appendix B.

r ­ 

### bData collected 

## on these dateswere used 

### by LMS

### to

### compute w-ratios

for 

### striped 

### bass life-stages. CSum

### of volumes of 

### all samples and 

### total number of

### samples collected

during 

### the day 

### or night, 

### after excluding outliers.

### V-51

from sampling programs that 

### were farmore intensive than that conducted at Roseton in 1974. Eighty-four intake samples were used to compute the 1975w-ratios for Roseton (Table V-14). The smallest volume sampled at Roseton during the day or night on any date in 1975 (125.7 m3 on the night 

### of June,9)

### was greater than the largest volume sampled during any 

### similar period in 1974. On each night during both years more samples were collected at the Indian Point intake and discharge stations than 

### were used by LMS to compute 1974w-ratios for Roseton (Table V-15). On most 

### of these nights the totalvolume sampled exceeded the total volume (422 

### m

### 3 of the 1974Roseton intake samples. Only at the Bowline intake on June 10, June 

### 17, and June 18, 

### 1975were total day 

### or night sample volumes
as low as the volumes sampled throughout the 1974entrainment season 

### at Roseton (Table V-16). In Section 

### 1.1 we discussed the fact that ichthyoplankton distributions 

### intheBudson River are patchy, 

### or overdispersed. consequence of this over-dispersion, the probability that any particular intake sample willcontain zero organisms, even when organisms 

### are in factbeing entrained, 

### is much higher than it would be 

### As a

if organisms were randomly distributed throughout the intake water. Therefore, given the 

### low sampling effort, it 

### is not surprisingthat very few stripedbass were collected at the Roseton intake in 1974. 

### We have determined from 

### LMS' data tapes (Appendix 

### B) that the 

### 17 samples used to cmpute the w-ratios for 1974 contained a total of three striped

### bass yolk-sac 

### larvae, allcaught in the single sample available for June 

### 4,and 

### two post yolk-sac larvae, both caught 

### at night on July 2. 

### In our opinion no meaningful 

### estimates of the abundance of stripedbass ichthyoplankton inwater entrainedby the Roseton plant
can be obtained from the data collected at the Roseton intake in 

### 1974. No faith can be placed in the w-ratios calculated from these data. We have not used them to compute any W-factors.

### 4.4

1974 

### V-52
_. .
USE OF'SAMPLES COLLEClXD 

### AT INDIAN POINT 

### RIVER STATIONSF AND G NYU collected ichthyoplankton samples 

### at seven river stations in and 

### 1975 (Exhibit 

### UT-3, Fig. 

### 3-IV-8). 

### LMS used data from all
seven 

### stations to compute the densities of striped bass life-stages in the.vicinity of the Indian Point plant. These river densities were then used to compute the w-ratios. In addition, data collected at three 

### of the seven stations, stations 

### B, 

### D, and G, 

### were used 

### as substitutes 

### for intake and discharge stationswhen the daytime w-ratios
were calculated (no plant samples are collected during the daytime at Indian Point

### 1. According to Table 

### 1-5 of NYU's Progress Report for 

### 1975 (New York University 

### 19771, Stations A through 

### E are all located between river 

### miles 41 and 

### 43 (the plant itself 

### is located at 

### RM 42). Stations F and G, however, 

### are located atRM 

### 39, three 

### miles downriver from the plant. We believe that stations 

### F and G 

### are too far from the Indian Point plant tobe considered "near-field" stations and therefore we have not used data collected 

### at these stations to compute W-factors. According to

### p. 

### 3-IV-20 of Exhibit 

### UT-3, the hydraulic model studies performed 

### at LaSalle Hydraulic Laboratory show that 

### "water
comes to the Indian Point intakes from a band about 

### 200 to 

### 350 

### Et (61 to 

### 107 m) wide 

### along the east shore 

### of the river during 

### the ebb and flood stages." 

### At slack tide, water 

### is drawn from 'la radius of approximately 

### 1600 ft

### (488 m) from the intake." It 

### is for this reason that 

### we chose stations

### D and 

### E rather than stations 

### C and D as substitutes 

### for intake and,discharge stations. Although station C is directly opposite the Indian Point intakes (Exhibit 

### UT-3, Fig. 

### 3-1V-8), it ismore than 

### 1600 feet from those intakes, according to Dr. O'Connor 

### of NYU (Transcript p. 

### 8527). Since station

### E is closer to the shoreline than station C (Exhibit 

### .UT-3, Fig. 

### 3-IV-81, in theory it should be more representative 

### of the water actually entrained by the plant. We say "in theory" because in practice no consistent differenceshave been observed among the densities of entrainable striped-bass life stages at any 

### of river stations A through 

### E. According to Table 7-5 of NU'S Progress Report

## v-53

for 1974 

### (New York University 19761, in 1974 significantly lower densities 

### of striped bass 

### larvae (= 

### post yolk-sac larvae) were observed 

### at station

### G than at station

### B. Otherwise, 

### no significantdifferences among stations

### were observed. According to Table 7-6 

### of 

### NYU's 1975 Progress Report 

### (NewYork University 19771, 

### in 1975

### no significant differences inabundance among 

### stations were observed for any striped bass life-stage. These results appear to indicate that, 

### at least for striped bass, it 

### does not make a 

### great deal 

### of differencewhich river stations 

### are chosen to represent the plant.

4.5 

### INTAKE AND DISCHARGE SAMPLE 

### VOLUMES 

### AT INDIAN 

### POINT In 1974 and 1975, the volumes 

### of samples collected at 

### the Indian Point intake and discharge stations were not measured with flowmeters. Instead, 

### volumes were estimated indirectly, as they are at 

### Bowliue and Lovett. However, unlike 

### US, 

### NYU did

### not use 

### actual measurements of 

### intake 

### or discharge canal velocities to estimate the volumes sampled. The volumes were calculated assuming that at 

### full flow 

### the intake velocity atboth Indian Point Units 

### 1 and 2 

### is 1.0 feet per second, and that 

### the velocity 

### at 

### all discharge stations 

### is 4.5 feet 

### per second (Exhibit EPA-1411. These velocities were multiplied by a 

### flow factor equal to the 

### fraction of the maximum possible flow observed on each sampling date (Exhibit EPA-141). The 

### velocities used to 

### compute sample volumes are incorrect; 

### NYU's applicaton of the flow factors 

### is also incorrect. According toTable 

### 1-2 of 

### NYU's Progress Report for 1975 (New 

### York University 19771, the maximum design velocities at the 

### Indian Point Unit 1 

### and 2 

### intakes are, respectively, only 0.7 

### feet 

### per second and 0.9 

### feet per 

### second. The 

### design velocity at discharge stationD-1 

### is 4.4 feet per second, only slightly smaller than the 

### assumed value. However, the 

### design velocity at 

### discharge station

### D-2 is substantially Smaller, only 3.4 feet 

### per second. 

### The velocities presented in Table 1-2 

### of 

### NYU's 1975 Progress Report are those expected atmean low water in 

### the Hudson. 

### According to the 

### heading of Table 1-2, 

### velocities
are expected to be 10%lower than the stated values at 

### high slack tide and 

### 5%lower at low 

### slack tide.

### v-54

On Transcript pp. 8244-45 Dr. O'Connor of 

### NYU confirmed that the designvelocities 

### are the velocities that should have been used:



### MR. KURENT: If it 

### were a full flaw situation- that 

### is, 100 percent operation- would 

### you use the one foot per second 

### at the intake and the 4.5 feet per second 

### at the discharge?


### DR. 0'CCN"NR: 

### No. According to the designvalues we had 

### at the 

### time, 

### we _- would have used .9 feet per second 

### for the Unit 2 intake and 

### .7 feet 

### per second for the 

### Unit 1 intake.

On Transcript pp. 8254-55, 

### Dr. O'Connor stated that, instead of the designvelocities contained in

### NYU's 1975 Progress Report, the velocities contained inExhibit EPA-141 

### were used in the preparation 

### of the utilities' testimony for these hearings: -_


### MR. KURENT: Although Table 72 does reflect less than full 

### flow was considered, did you still assume 

### as stated in

?- Exhibit 141 that full flaw velocity

### was one foot per second?


### DR. O'CONNOR: Yes, we did 

### at the 

### timeof 

### the preparation of this report. That value, however, 

### was corrected 

### earlier this spring

### when the data

# - when the plant operating data 

### were being reevaluated inpreparation for submission 

### of the Federal Energy RegulatoryCoxmission 

### report 

### on Cornwall.


### MR. KUREW: Are you saying that you used the one-foot per second velocity for the preparation of the testimony that 

### was submitted on July 

### 11, 1977 for thishearing?


### DR. O'CONNOR: Add (sic) correctedby the percentage flw, 

### yes.

~ The 

### errors introduced by 

### NYU's assumption 

### of erroneously high intake .velocitiesare partiallyoffset by an erroneous application of flow

factors to correct for cooling water flows of less than 100%of capacity. 

### When any particular circulator pump is operating, that pump 

### is operating at 100%of capacity, regardless of whether other pumps 

### are operating or not. Since each pump at Indian Point Units 1 and 

### 2 is located ina separate forebay, the intakevelocitywithin a forebay containing an operating pump should always be 100%of the design 

### flow.

### v-55

Since an intake sample can be collected only 

### from a forebay containing
an operating pump, no flow factors need tobe applied inorder to estimate the volumes of the intake samples. There 

### is no such offsetting

### error in the computation of the discharge sample volumes. When the actual cooling 

### water flow 

### is 

### less than 100%of capacity, the design velocities presented inNYU's 1975 Progress Report for discharge Stations D-1 and 

### D-2 must be adjusted downward using the 

### flaw factors. Primarily because of the erroneously 

### high values used for sampling velocities at theUnit 1 intake and at discharge Station

### D-2, the nighttime densities (and the w-ratios as 

### well) 

### of stripedbass life stages computed by 

### LMS for the Indian Point plant 

### in 1974 and 1975

### are erroneously 

### low. 

### As no plant samples 

### are used to compute daytime w-ratios, these 

### are not affectedby the errorsdiscussed 

### inthis secton.



### 4.6 COMPARISON 

### OF SURF'ACE AND 

### MIDDEPTX SAPIPUS COLLECTED INBOWLIlJE POND TO 

### SURFACE, MIDDEPTH, AND BOITOM SAMPLES 

### COLLECTED 

### W THE RIVER During May of both 1974 and 

### 1975, pairs 

### of river and plant data sets collected on the same day were not available 

### for Bowline. Since paired 

### sets of river and Bowline Pond data

### were available, 

### LMS calculatedw-ratios using the pond data as a substitute for data collected 

### at the plant itself. We agree that it 

### is valid touse data collected inBowline Pond to calculateW-factors. 

### In fact, as 

### is stated inSection 

### 4.2, we believe that these data 

### are superior to data collected at the Bowline intake and that they should be used to compute allW-factors for Bowline. 

### We disagree, however, with themethod used by 

### LMS to compute w-ratios using the pond data. 

### Two stations inBowline Pond, referred to 

### as Bowline Pond 

### Long (BPL) and Bowline Pond Short (BPS), 

### were sampled 

### in 1974 (Exhibit UT-7A, p. 9.1A-7). Only Bowline Pond Long 

### was sampled in 1975 (Exhibit UT-7A, p. 9.1A-7). Surface, mid-depth, and bottom samples were collected at station

### BPS, but, due to obstructions 

### on the bottom of the pond, only surface and mid-depth samples were collected at station BPL. LMS computed pond lower layer densities for 1974 using bottom

### V-56
samples collected 

### at BPS and mid-depth samples collected 

### at both BPS and BPL. Similar 

### lwer Layer densities for 1975 were computed using only themid-depth samples collected at Bowling Pond Long. The correspcmding 

### lower layer densities for the 

### river were computed using both mid-depth and bottom samples. 

### The highest densities 

### of striped bass eggs and larvae inthe river


### are generally found 

### near thebottom, especially during the daytime (Exhibit 

### UT-4, Sections 7.3.6, 7.4.1.6, and 7.4.2.7). 

### If the pattern 

### of distribution

### of these life-stages inBowline Pond 

### is similar tothat found inthe river, then the 

### lower layer w-ratios computed by 

### LMS using the 1975 pond datamay be biased. 

### If eggs and larvae in

### Bowline Pond


### are more abundant at the bottom than at mid-depth, and if bottom samples had been collected at station

### BPL, then higher 

### lower layer densities, and therefore higher 

### lower layer w-ratios, would have been computed. In the absence 

### of bottom samples, 

### it 

### is not possible to show whether the 

### vertical stratification

### of ichthyoplankton inBowline Pond is similar to or different from that found inthe River. However, there 

### is 

### no reason tobelieve that these organisms behave any differently inthe pond than they do inthe 

### river. Eggs 

### willstill tend to sink unless resuspended by turbulence. Larvae 

### will still migrate toward the bottom during the day and disperse throughout the 

### water column at night. This fact

### was acknowledged by Dr. O'Connor 

### of 

### NYU on Transcript p. 8548:

DR. 

### O'CONNOR: 

### As pointed out by Dr. Englert, the 

### larvae probably undergo the same sorts of diurnalmigration inthe pond, those that might occur there 

### as they undergo inthe 

### river

# .
However, Dr. O'Connor claimed that the turnover 

### rate of Bowline Pond (i.e., the 

### rate atwhich water inthe pond 

### is entrained through the Bowline plant and replaced by water drawn from the river) is so high that eggs and larvae-would not have the to accumulate at the bottom (Transcript pp. 

### 8539-41) : ,.. .

,-

,

### v-57



### MR. KURENT: Would you expect the concentration of striped 

### bass eggs to be highest 

### near the bottom given the conditionsprevailing in that pond?


### DR. 

### O'CONNOR: 

### Iwould not expect that. First 

### of all, there 

### is no evidence that stripedbass spawn 

### inBowline pond. Therefore, to have large numbers 

### of eggs inthe pond 

### is unlikely. The entrance to the Bowline pond, I believe, has a depth only of five feet. Therefore, any stripedbass eggs emerging the pond 

### are going to be those from about the top five feet of 

### water inthe 

### river and the turnover of water inthe pond 

### is sufficiently rapid; 

### I would believe that there would be insufficient time for the eggs to sink to the bottom.


### MR. 

### KURENT: Larvae that would be present 

### inthe por-d would be more likely to 

### remain at or near the bottom amongst the objects near the bottom; wouldn't they?


### DR. O'CONNOR: 

### I honestly couldn't say. First of all, there 

### are no samples from the very bottom strata. Larvae which entered the pond, again, willbe those which 

### are capable of coming over that sill at the entrance to the pond. And since most of the data, tomy recollection, indicate that yolk sack larvae and the younger of the post 

### yolk sack larvae tend to remain in the bottom and middle depths of the river, that the numbers coming over the sill would be relatively 

### small. And, again, turnover in the pond would probably be sufficientlyrapid to prevent them from forming any great accumulation inthe bottom. Since there 

### is no information available on the sinking rate of striped bass eggs inBowline Pond, there is no way to verify or disproveDr. O'Connor's claimwith respect to this life-stage. However, it seems likely that there 

### is in fact sufficient time for the activelymigrating larvae to move to the bottom. Diurnal migration occurs over a period of only a 

### few (at most 

### 12) hours: larvae present inthe upper strata during the night are capable of moving to the bottom strataby the next afternoon. Therefore, unless the average residence time of larvae present inthe pond is less than 12hours, they willbe able tomigrate to the bottom before being entrained.

### V-58

We believe Lhat given the tendency of organisms to distribute themselves within the pond inthe 

### same way they 

### are distributed inthe 

### river, and given the lack 

### of data on the actual vertical distribution 

### of organisms inthe pond, 

### it 

### is better to 

### assume that the pond and 

### river distributions are the same. Therefore, inour calculationof W-factors 

### for Bowline we have compared data collected 

### at the surface and at mid-depth inBowline Pond to data collected 

### at the same depths in the river. 

### We have excluded all of the data collected 

### at the river bottom. For consistency 

### we have 

### also excluded the bottom samples collected 

### at station

### BPS 

### in 1974.

4.7 ZEm DIVIDED BY ZER!3 

### EQUALS 

### ZERO At Bowline in 

### 1974 no stripedbass eggs 

### were caught during the daytime 

### at either the river or the Bowline Pond stationsassigned to the 

### lwer layer (Exhibit UT-3, Table 3-IV-22). Similarly, 

### at Roseton, both in 

### 1974 and in

### 1975, no eggs 

### were collected at night ineither the 

### river samples or the plant samples assigned to the upper layer (Exhibit 

### UT-3, Tables 3-1V725 and 3-IV-26). In all cases inwhich the seasonal plant and river densities

### are identical and non-zero, the w-ratio 

### is equal to one. Therefore, it


### seems logical to 

### us that whenever these densities are both equal to zero, the value 

### of the w-ratio should be set equal toone. However, in 

### all three of the above cases 

### at Bowline and Roseton, LMS assumed a w-ratio of zero, implying no entrainment by the plants regardless of whether 

### or not there actuallywere eggs present inthe river. 

### Dr. Englert of LMS offered two justifications for the assumption that zero divided by zero equals zero. First, he argued that 

### it is valid to


### assume that w 

### is equal to zerobecause the finding of no organisms in the intake samples indicates that none werebeing entrained (Transcript pp

# .8383-85, emphasis added) :



### MR. KURENT: Since the river and the plant concentrations are identical, that 

### is, inyour ratio the numerator and the denominator for the equation are the same, shouldn't the w ratio logicallybe 

### l? --\

_ -

### v-59

DR. 

### ENGLERT: 

### A zero divided by 

### zero is an undefined quantity 

### mathemat

### i

### ca11 y 

### MR. 

### KURENT: Undefined, but you define the w 

### ratio as zero 

### as a result 

### of that calculation.


### MR. 

### FRIEDLANDER: He had a remainder of 

### an answer to 

### give, hismouth 

### was open. 

### DR. 

### ENGLERT: As we indicated, the

### w ratio 

### is an 

### estimate of the number 

### of organisms entering the plant divided by the number 

### of organisms out inthe river. And keep inmind, 

### of course, that 

### we 

### are using this w ratio inthe computation 

### of power plant impact. So that 

### it would 

### seem ridiculous to assume a value 

### of 1 for the

### w ratiowhich is going to be used to estimate this impact when, in fact, no organisms entered the plant.


### MR. KURENT: Isn't it equally as ridiculous 

### to determine this equation to result ina zero? 

### DR. ENGURT: 

### No, it is 

### not ridiculous for the 

### very reason that I just explained.


### MR. KURENT: Shouldn't the mean concentrations actually be excluded if you don't choose to

### make the conservative assumption and call it a 

### l? Since it 

### is mathematically a likenumerator and a like . denominator, shouldn't you either consider it a 

### 1

### or exclude this data indetermining w ratios?
DR. ENGLERT: 

### NO.
As I indicated 

### inone of my previous 

### answers, what 

### we are trying to do here is to estimate power plant impact, and there 

### is no reason to assume 

### a w ratio 

### of 1when infact

### we didn't find any organisms of that life stage at that plant in either the intakeor discharge samples.


### MR. 

### KURENT: How can you assume any w ratio

### on the basis of no organisms? Isn't the 

### w ratio some measure 

### of withdrawal 

### or comparison of concentrations inthe river and the plant? 

### If you have no organisms, how can you use those zeros to give us a withdrawal ratioof zero? 

### It is likemaking something from nothing; isn't it?

### V-60



### DR. ENGURT: The purpose 

### of the

### w ratios as I have indicated several times is to aid us inestimating power plant impact. 

### Now, 

### as I have saidrepeatedly, now if we are trying to estimate the number 

### of organisms entering the plant based on this

### w ratio, 

### I see no basis for assuming a value 

### of 

### 1when in fact 

### we did not find any organisms ineither the intake or the discharge 

### of the plant. 

### The fact that

### we didn't find any organisms means that there

### were none entering.


### Later, 

### Dr. Englerr offered a second justification: since Roseton 

### and Bowline 

### are at the extreme ends 

### of the spawning range 

### of striped bass,
no eggs 

### were present inthe river in the vicinity 

### of these plants. We quote from 

### Transcript pp. 8386-89 (emphasis added):



### MR. KURENT: Didn't you state earlier that the purpose 

### of establishing criterion of wanting ten organisms per thousand cubic 

### meters was to show that you 

### were sampling at a time when 

### in fact organisms 

### were present in the river in significant concentrations? The fact that you have a zero concentration inthe river and at the plant 

### runs contrary to the stated purpose 

### of computing the 

### w ratiousing the criteria that you established.


### DR. ENGLERT: As I recall, 

### I said criterionwere established to 

### allow us to 

### look at the period of appreciable abundance 

### ineither the plant 

### or the river and to 

### limit ourselves to those samples. But, infact, what happened in these cases 

### is that we note that there were eggs and/or larvae inthe Hudson River during the sampling periods because these samples covered the entireperiod 

### of abundance 

### of those organisms.
The fact that we didn't find them at the plant or the river simply means that they weren't 

### inthat particular plant region and inthat particular plant. So I don't see any problem with assuming a value 

### of zero in this case. 

### MR. 

### KURENT: Does the fact that you didn't find them in the river mean they weren't inthe river? . -I

, --,

### V-61



### DR. ENGLERT: 

### It means in

### terms of the data, it means that we didn't find them inour samples.


### MR. KURENT: Does the fact that you didn"t find them 

### in the rivermean that they weren't inthe 

### river? DR. ENGLERT: We know that during that same period there 

### were organisms of that lifestage

### of striped bass inthe Hudson River.


### MR. 

### KURENT: Does the fact that you didn't find them at the intakemean that they weren't entrained into the intake? 

### DR. ENGLERT: It meaas that 

### we did not capture any 

### inour samples at the intakes.


### HR. KURENT: And you are not able tojudge whether they were entrained into the intake; are you? DR. ENGLERT: 

### I am not able 

### to judge that based on the sampling program that 

### was ineffect during that time.


### MR. KURENT: You can assume, even though you found zero 

### in the river, that there 

### were still organisms present 

### inthe river; why can't you assume that even though you didn't find them at the intake that therewere still organisms present going through the intake?


### DR. ENGLERT: Because I

# - because for the plants that we are looking 

### at, the Bowline and Roseton plants which are on the extreme of the spawning regime of the striped bass, it 

### is not unexpected tobelieve that there were no organisms in that plant region. 

### I ssid that there 

### were organisms of that lifestage in the Hudson River as a whole. 

### I say that based on OUK sampling program, we beli'eve that those organisms were not present in the Bowline or Roseton area at that 

### time, 

### or that year.

We find both 

### of Dr. Englert's rationalizationsto be exceedingly unconvincing. First, the fact that no eggs were captured inthe plant samples does not imply that no eggs were entrained (as Dr. Englert claimed inthe emphasized quote on p. 

### V-61). It simply means that none
were collected in the tiny fraction of the intake water sampled by 

### LMS (as 

### Dr. Englert admitted inthe emphasized quote on p. V-62). 

### It clearly is possible that eggs were present but simply not collected.

### V-62

Given the pitifully inadequate sampling effort expended 

### at the Roseton intake in 

### 1974 (Section 

### 4.3 

### of this testimony), it would have been surprising 

### if any 

### eggs had been collected. 

### We noted in Section 

### 4.3 that the total volume of all the samples collected at the Roseton intake 

### on dates used to compute 

### 1974 w-ratios 

### for stripedbass waa only about 

### 400 

### m3 On the two dates used by LMS to compute 

### 1974 w-ratios for eggs, May 

### 9 and 

### May 22 (Exhibit 

### UT-3, p. 

### 3-IV-511, only seven samples 

### were collected 

### at the plant and only 

### 135 m3 

### of intake water
were filtered (Table 

### V-14). 

### This total volume 

### is approximately equal to the volume 

### of a single river sample. 

### Dr. Englert's second argument 

### is flatly contradicted by the facts. Striped bass eggs 

### were collected inthe Bowline and Roseton regions 

### in1974 and 

### 1975 by both 

### LMS and TI. TI collected striped bass eggs in the Croton Haverstraw 

### (CHI region, where the Bowline plant 

### is located, and inthe Poughkeepsie 

### (PK) region, where the Roseton plant 

### is located, both 

### in 1974 and in 

### 1975 (Exhibit 

### UT-4, Tables 

### 6.2-1 and 6.2-5). During both 

### years eggs were collected 

### in every river region except 

### Yonkers 

### (YK). If striped bass eggs 

### were infact present

+-. throughout the river, both above and below the Bowline and Roseton plants, then they must have been present inthe vicinity of each plant. 

### LMS' data shows that eggs 

### were present atboth locations, although they 

### were not caught 

### at all depths at 

### all 

### times 

### of day. For example, at Roseton in 

### 1974 and 

### 1975 eggs 

### were found 

### in river samples collected 

### at night in the 

### lower layer and during the day inboth upper and lower layers. Can 

### it be that during the day eggs 

### were present throughout the 

### water column but at night they migrated to the bottom? Considering both 

### TI'S and 

### LMS' data, the only reasonable conclusion that can be drawn 

### is that even though during some 

### timeperiods at some depths no eggs 

### were collected in the vicinity 

### of the Bowline 

### and Roseton plants, eggs were in fact present in the vicinityof the plants at those depths during those time-periods. 

### Is the fact that eggs were probably present in the river, even though none were caught, further evidence that it 

### is justifiable to
assume that the w-ratio is equal to zero? Dr. Englett thinks so9as evidenced by Transcript pp. 

### 8417-23:

### V-63



### MR. KURENT: Since your sampling reflected the presence 

### of 

### no stripedbass eggs inthe river inthe upper layer 

### at night, 

### can we then infer that if eggs were in fact present in the river in the vicinity of the plant, even though 

### LMS' sampling program missed them, that it wouldn't 

### be valid to assume then that 

### zero over zero equals zero?


### DR. ENGLERT: 

### NO. In 

### the context 

### of the answer that 

### I just gave 

### a 

### few questions ago, that would in fact support the idea of 

### a 

### zero 

### w ratio because, as 

### I explained, any number divided by zero 

### is infact 

### zero. So that if therewere eggs there and we 

### were able to measure that river concentration, if 

### we divided that by the zero catch inthe plant, 

### we would get again 

### zero. 

### MR. KURENT: Let me 

### restate the question and see if you can interpret it differently

# . I said, then can we infer that 

### if eggs 

### were in fact present inthe river inthe vicinity 

### of the plant, even though your sampling program missed them, that it would not be valid to assume zero over zero to equal to zero; 

### is that the question 

### as you understand it?


### DR. ENGLERT: Yes, that's the question 5 just answered.


### MR KLRENT: If it can be shown that striped,basswere present inthe river 

### insignificant numbers but you missed them, which you apparently did, wouldn't it be 

### equally logical 

### to assume that they 

### were present inthe vicinity of the intake and you missed them there as well?


### MR. FRIEDLANDER: Objection. You asked that same question yesterday. This 

### is on page 

### 8388.
I willread the question and 

### we will see the parallels between what was asked yesterday.


### MR. KURENT: I am asking a new question.


### MR. FRIEDLANDER: You are not. You are asking the same questions as you asked yesterday- It's on page 8388, lines 

### I1 to 16. I would appreciate your distinguishingthe difference between that question and the questionyou just asked. Talk about a waste of time.V-64



### MR. 

### RURENT: Mr. Friedlander is correct that I asked that question yesterday, but 

### I looked at the answer 

### inthe record that 

### Dr. Englert gave, and the answer he just gave 

### is different than the answer he gave yesterday and 

### I would like

~ him to explain the difference. If I may read his answer from 

### 8388. He said 

### inresponse



### __ to the question yesterday: "Becayse for the plant that we are looking at, the Bowline and Roseton plants, which are on the extreme 

### of the _- spawning regime of the striped bass, 

### it's not unexpected to believe that there 

### were no organisms inthe plant region."


### MR. FRIEDLANDER: And the answer continues.


### MR. KURENT: 

### "I said that there were organisms of that life stage 

### inthe Hudson River as a whole. 

### I say that based 

### on our sampling program. 

### We believe that those organisms were not present inthe Bowline 

### or Roseton area at that time or that 

### year.I' 

### I appreciate the reference, Mr. Friedlander. 

### Now, that seems to differ with the answers you have given me 

### this morning. So 

### I am asking you again, if in fact there were organisms inthe river contrary to your assumption inyesterday's answer

# - DR. ENGLERT: First 

### of all, 

### I see no inconsistency inthe answer. You are askingme now towork on a hypothetical, as I understand 

### it.


### MR. K'3RENT: 

### The hypothetical being?


### DR. ENGLERT: That there were organisms inthe river which we did not capture.


### MR. KURENT: Perhaps I could assist you inthat. Perhaps YOU could 

### look at Exhibit 

### 4,Table 6.2-5.


### Do you have 

### a copy of that? DR. ENGLERT: I am 

### sorry, I don't.


### JUDGE

### YCST: I have provided the witness 

### with a copy 

### of the exhibit.

### V-65



### MR. KUREW: 

### Thank you, 

### your Honor. That table, Dr. Englert, 

### sets forth the regional densities of stripedbass eggs during 

### 1974 in the Hudson River estuary

# . Now, isn't the Roseton plant inthe river section delineated by Poughkeepsie, 

### PK?
MR. FRIEDLANDER: Your Honor, 

### I object to this line

### of questioning. These materials 

### are on abundance and distribution, which 

### was examined by 

### Mr. Strong 

### for two weeks.
Dr. Englert is not particularlyprepared to respond on these 

### materials


### We were told that 

### we would be questioned 

### on W ratios today

# . JUDGE YOST: I willoverrule your objection. 

### I know what you 

### are saying, but on the other hand I don't think it's improper to refer a witness to an exhibit inthe testimony that speaks for itself. 

### Be doesn't have to know about it toread the numbers.


### MR. 

### KURENT: Dr Englert, if you look at the data presented under the column marked "Poughkeepsie," which 

### is the Roseton plant vicinity, 

### isn't it apparent that therewere striped bass eggs recovered 

### invarious quantities throughout the sampling period around the Roseton plant during 

### 1974? DR. 

### ENGLERT: The figures that you are showing

# -- this table, 6.2-5, shows regional densities for the Poughkeepsie region, which extends seven or eightmiles, 

### as I recall. 

### It's rather large region and according to that table, there 

### were some eggs collected within thst region.


### MR. KURENT: How do you square this with the fact that you have assumed that there were no stripedbass eggs 

### inthe Roseton plant region? DR. ENGLERT: 

### I didn't assume that there were no eggs in the Roseton region. 

### I computed 

### a w ratio and I computed concentrations in the river inthe immediate vicinity

### of the plant based 

### on transsect sampling that was done 

### by our company inthe river immediately adjacent to.the Roseton 

### power plant.
And those results are presented in Table B-21.

### V-6

### 6



### MR. KURENT: Dr. Englert, if you would alsolook at the 

### chart, isn't it apparent that inregions on either side of Poughkeepsie there were eggs present 

### invarious densitiesas well?


### DR. ENGLERT: 

### Yes, that's true. 

### MR. KURENT: Considering the 

### fact that they appear 

### to exist-inboth regions on either sideof Roseton and 

### in the region inwhich Roseton 

### is located, isn't it logical to assume that they existed inallthese contiguous regions and therefore infront 

### of the Roseton plant?


### DR. 

### ENGLERT: As I 

### am trying toexplain, the sampling that 

### we were basing the

### w ratios on 

### is the transsect sampling and the sampling at the plant that 

### was performed during 1974. Now, it wouldn't bother 

### me at all if infact

### we had found eggs inthe river or if 

### infact there 

### were eggs inthe river during 1974 because the important point 

### is that 

### we didn't find any 

### inthe plant. And infact, it would simplify our discussions 

### if there were some eggs 

### inthe river because it would mean that

### we have a non-zero number todivide intothe zero that 

### we found intheplant and itwould still giveus the result, which 

### is 

### a zero w ratio. 

### Dr. Englert's argument 

### is fallacious. If eggs were present inthe river but not collected, then eggs may just 

### as wellhave been present at the plant but not collected there either. In fact, since the sampling effort at power plant intakes 

### is nearly always substantially less than that at the corresponding river stations, it ismuch 

### mere likely that no eggs would be collected at a power plant intake, even though eggs 

### were being entrained, than it 

### is that 

### no eggs would be collected inthe river, even though eggs were present there

### No eggs were collectedat the Roseton intake 

### in 1974. We have previously noted that only 135

### m3 of 

### water 

### were filtered at the Roseton intake on the two sampling dates in 1974 that metLMS' criteria forw-ratio analysis for stripedbass eggs. InTable 

### V-17 we present 5 the volume 

### (m ) 

### of the river samples collected on these dates, and thenumber of these samples that contained stripedbass eggs. Even 

## . ­

.-

though theaverage volme of each sample collected from the river on these two dates was greater than the total volume sampled at the I

### V-67



## Table V-17. 

## Numbers of 

## samples collected at 

## Roseton/Danskamner river transect stations 

## on May 

### 9 

## and May 21, 

### 1974a

### that

## contained stripedbass eggs
Average sample Number of 

## Number of 

## samples Date Nb 

## volume (m3) 

## eggs collected containing eggs May 9 

### 36 199.1 

### 11 1 

### May 

## 21 37 202.6 

### 39 

### 3


## aDatacollected on these dates were used by 

## LMStocalculatew-ratios

for striped

## bass eggs. bTotal 

## number of

## samples collected at all 

## stations and 

## depths during

daytime 

### and 

## nighttime.

### V-68

intake on both dates combined, only 1out of 36 (3%)river samples collected on May 

### 9 and only 

### 3 out of 

### 37 (8%)collected on 

### May 21 actually contained eggs. Clearly, even if the density of eggs inwater entrainedby the Roseton plant on these dates

### was inrealityequal to the density

### of eggs inthe 

### river, the probability that any would be collectedinthe 19

### m3 of 

### water sampled on May 9 or the 116

### m3 sampled on 

### May 21 (Table 

### V-14) isnegligible.

,..._ 

### We believe that 

### LMS' assumption that zero divided by zero 

### is equal to zero 

### isunjustifiable. W-factors 

### are not measures of the numbers 

### of organisms entrainedby power plants, but measures 

### of the density

### of organisms 

### inpower plant cooling

### water relative to their densityinthe river inthe vicinityof the plant. 

### If no organisms 

### are collected at either location, themost reasonable assumption is that the plant and river densities

### are equal. ThereforeW-factors should be 

### set equal to
one. Since

### we do not compute 

### a W-factor unless at least ten organisms


### were collected ineither the plant or the river samples, 

### we do not encounter this problem when calculatingW-factors using theMU method. 

### A similarproblem does occasionally occur inthe applicationof the 

### GBC method: if the computed values 

### of the upper and lower layer w-ratios


### are both 

### zero, then the value of the factor

### A computed from Eq. 

### (V-10) (Section 2.2.2), 

### i.e., (wu

# - wL)/(wu 

# +w,>, 

### isequal to zero divided by zero. Interestingly, inthis case

### we assume that 

### A is equal to 

### zero, for the 

### same reasonwe 

### set forthabove when arguing that LMS' w-ratios should be 

### set equal to one. 

### If w U and w

### L are equal and nonzero, then

### A is equal tozero. Thus, zero divided by zero 

### is equal toone, but zerominus zero divided by zero 

### is equal tozero.

,^

### V-69

5. DISCUSSION

Even a casual inspection of Tables V-3 through 

### V-12 reveals that many of the GBC and 

### MU W-factors computed for the same population, life-stage, year, and time of day differ substantially 

### from each other. This 

### is not unexpected, given the differences inassumptions and computational procedures between the two methods. The 

### HU method 

### assumes that the gears used to collect plant and river ichthyoplankton samples have equal collection efficiencies. 

### All differences between observed plant 

### and river densities are attributed to real differences between the abundance of organisms ina power plant intake and their abundance in the river cross-section infront 

### of the plant. The 

### same 

### is not true 

### of the 

### GBC method. The GBC method attributes all differences between the abundance of organisms in the upper layer of 

### an intake

### bay and their abundance inthe upper layer 

### of the river to the effects of gear bias. It is the relative magnitudes 

### of upper and lower layer w-ratios and the vertical stratification

### of organisms inthe river that determine the value of a W-factor calculated using the GBC method.

' Because 

### of the differences between the two methods, violations of assumptions that affect one 

### willnot affect the other. 

### If, for some population and life-stage, the efficiency of the gear used to collect samples at a particular plant 

### is markedly different from the efficiency of the gear used to collect the corresponding

### river samples, 

### a bias willbe introduced into the MU W-factor but 

### not into the GBC W-factor. Conversely, 

### if there 

### is a lateral trend inthe distribution of organisms belonging to a given species and life-stage 

### in the upper layer of the river (e.g., they are more abundant near the east or west shore than 

### in the channel), the GBC W-factor willbe affected but the 

### Mu W-factor willnot. Equally important, and for exactly the same reasons, the two methods are affected differently by sampling errors. 

### If, by chance alone, the observed density 

### of organisms 

### of a particular population and life-stage inthe river samples 

### is lower than the observed density inthe plant samples, although the true plant and-- 

## v-70

river densities are the same) then the MU W-factor willbe erroneously high. The GBC W-factor computed from the same data may be erroneously high, erroneously low, or unaffected, depending 

### on 

### how the relative magnitudes of w 

### U and w

### L

# ,and the fractional distribution of organisms inthe upper and lower layers 

### of the river 

### are affected by the sampling error. Since each run 

### of the Empirical Transport 

### Mode1 employs 20 U-factors (five plants x four life-stages), the differences observed within individual GBC/MU pairs are unimportant as long 

### as neither method produces consistently larger or smaller W-factors than does the other. Table 

### V-18 presents 

### a comparison 

### of the GBC and MU W-factors for striped bass, white perch, Atlantic tomcod, bay anchovy, and Alosa.



# - For each population 

### we tabulated the number of 

### times the GBC W-factors

### were larger than the corresponding 

### MU W-factors. We then used the sign 

### test (Siege1 1956) to determine whether W-factors for any

. 

### of these species calculated using the 

### GBC method are on the average larger or 

### smaller than 

### similar W-factors calculated using the MU method. The MU W-factors for bay anchovy 

### are consistently larger than the GBC W-factors. This 

### result indicates that for this species one (or both) method(s) 

### is subject to some kind 

### of systematicbias. Unfortunately, 

### we cannot 

### determine the 

### nature of this bias, 

### and therefore we have no way 

### of knowing which set of W-factors corresponds most closely to reality. The practical effects 

### of the systematic differencebetween the 

### two sets are 

### an increase inthe range of conditional entrainment mortality rates obtained from the Empirical Transport Model, and a concomitant increase in the uncertainty 

### of conclusions drawn about the impact of entrainment on the Hudson River bay anchovy population. For the other four species, the sign 

### test revealed no significant difference between the number of GBC W-factors that are larger than the correspondingMU W-factors and the number that are smaller. Thus, for striped bass, white perch, Atlantic tomcod, and Alosa there is little overall difference between the two 

### sets of W-factors. Similar

### V-71



## TableV-18. Statistical significanceof differencesbetween the MU 

## and GBC 

## W-factors for 

## each species, 

## as determined from

.

## the sign 

## testa

Number of 

## Speci

## es paid 

## nf

. 

### Pd 

## Stripedbass 50 

### 25 1.00 (NS) 

## White perch 48 22 

### 0.67 

## (NS) At1anti

### c 

## tomcod

. 

### 21 10

I 

### 1.00 

### (NS) 

### Bay 

### anchovy 30 6' 

## 0.01e Alosa 

## spp. 43 

## 16'. 0.13 (NS)


## aMethod described by 

## Siege1 (1956). 

## bNumber of pairs 

## of MU and 

### GBC 

## W-factors calculated for the



## same species, life-stage, plant, year and 

## time of day, 

## excluding ties. cNumber of 

## pairs in

## which 

## GSC W-factor is

## larger than MU



### W-f

## actor. dTwo-tai 1

## ed test

# . esignificant at 1% level.

### V-72

conditional entrainment mortality rates should be obtained from the Empirical Transport Model regardless of which method supplies the input parameters. 

### For four out of five species, independent methods 

### of solving the


### same problem 

### (i-e., how to estimate a W-factor), each involving different simplifying assumptions and subject 

### to different sorts of biases, have led to similar results. Because the conditional entrainment mortality rates for striped bass, white perch, Atlantic tomcod, and Alosa produced by the 

### ETM are relatively independent of the method used to generate W-factors, they should be, inLevins' 

### (1966) terminology, "robust" with respect to these parameters. For these species conclusions drawn from the 

### ETM results, and management decisions based on those conclusions, should be relatively free from the effects of the various types 

### of 

### error6 that can affect 

### estimates of W-factors and w-ratios.

.,.'

t

### v-73

6. 

### REFERENCES CITED
Barnthouse, L. 

### W., J. B. Cannon, S. 

### W. Christensen, 

### A. �I. 

### Eraslan, 

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### T. 

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### W. Van Winkle. 1977. A 

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### on the Bowline Point Generating Station. ORNL/TM-5877. Oak Ridge National Laboratory, Oak Ridge, Tennessee. Carpenter, 

### E. J. 1979. Sampling ichthyoplankton. Exhibit 

### EPA-196. Cassie, 

### R. M. 1963. Microdistribution of plankton. Oceanogr. Mar. 

### Biol. Ann. Rev. 

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### Ecological Analysts. 1976a. Bowline Generating Station entrainment survival and abundance studies. 1975Annual Interpretive Report. Prepared for 

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### for Orange and Rockland Utilities, Inc. Exhibit 

### UT-3. Consolidated Edison. 1977b. Supplement 

### I to Influence of Indian Point Unit 2 

### and other steam electric generating plants
on the Hudson River estuary, with emphasis 

### on striped bass and other fishpopulations. Exhibit 

### UT-4. Consolidated Edison. 1977a. Influence of 

### Indian Point Unit 2 

### and other steam electric generating plants on the Hudson River estuary, with emphasis on striped bass and other fish populations. Exhibit UT-6. Central Hudson. 1977. Roseton Generating Station. Near-field effects of 

### once-through cooling system operation on Hudson River biota. Exhibit 

### UT-7. Orange and Rockland. 1977a. Bowline Point Generating Station. Near-field effects of 

### once-through cooling system operation on Hudson River biota. Exhibit UT-7A. 

### Orange and Rockland. 1977b. Appendixes-Bowline Point Generating Station. Near-field effects of once-through cooling system operation on Hudson River biota.v-74

'7 Levins, 

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.1 

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### .~ New York University. 1976. Effects 

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### of New York, Inc. New Pork University. 1977. Effects 

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### plant 

### on biota inthe Hudson River estuary. ProgressReport for 1975. Prepared for Consolidated Edison Co. of 

### New York, Inc. Siegel, 

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### for the Behavioral Sciences. McGraw-Kill, 

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### York. 312 pp. Smith, 

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### Int. Explor. 

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,-.

J